text:
archival research. The approach draws on principles of multilevel modeling to implement random intercepts and slopes that vary among dyads. The corresponding variance function permits the computation of a dyadic reciprocity correlation. The modeling approach can potentially be integrated with other statistical models in the field of social network analysis.},
  archivePrefix = {arXiv},
  eprint = {2005.09827},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Koster/Koster_2020_Dyadic Reciprocity as a Function of Covariates.pdf;/home/aleksandar/Dropbox/research/papers/kosterDyadicReciprocityFunction2020.md},
  journal = {arXiv:2005.09827 [stat]},
  primaryClass = {stat}
}

@article{kottCalibrationWeightingNonresponse2017,
  title = {Calibration {{Weighting}} for {{Nonresponse}} That Is {{Not Missing}} at {{Random}}: {{Allowing More Calibration}} than {{Response}}-{{Model Variables}}},
  shorttitle = {Calibration {{Weighting}} for {{Nonresponse}} That Is {{Not Missing}} at {{Random}}},
  author = {Kott, Phillip S. and Liao, Dan},
  year = {2017},
  month = jun,
  volume = {5},
  pages = {159--174},
  issn = {2325-0984},
  doi = {10.1093/jssam/smx003},
  abstract = {Abstract.  Calibration weighting can be used to remove bias when unit nonresponse is a function of one or more survey variables. This is done by allowing the mo},
  file = {/home/aleksandar/Zotero/storage/LMQHVTEX/3739022.html},
  journal = {Journal of Survey Statistics and Methodology},
  language = {en},
  number = {2}
}

@article{kovacevicPartySystemInstitutionalization2014,
  title = {Party System Institutionalization in {{Serbia}}},
  author = {Kova{\v c}evi{\'c}, Despot},
  year = {2014},
  volume = {10},
  pages = {63--72},
  journal = {Politi\v{c}ki \v{z}ivot}
}

@article{kowalska-styczenNoiseInducedUnanimity2020,
  title = {Noise Induced Unanimity and Disorder in Opinion Formation},
  author = {{Kowalska-Stycze{\'n}}, Agnieszka and Malarz, Krzysztof},
  year = {2020},
  month = jul,
  volume = {15},
  pages = {e0235313},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0235313},
  abstract = {We propose an opinion dynamics model based on Latan\'e's social impact theory. Actors in this model are heterogeneous and, in addition to opinions, are characterised by their varying levels of persuasion and support. The model is tested for two and three initial opinions randomly distributed among actors. We examine how the noise (randomness of behaviour) and the flow of information among actors affect the formation and spread of opinions. Our main research involves the process of opinion formation and finding phases of the system in terms of parameters describing noise and flow of the information for two and three opinions available in the system. The results show that opinion formation and spread are influenced by both (i) flow of information among actors (effective range of interactions among actors) and (ii) noise (randomness in adopting opinions). The noise not only leads to opinions disorder but also it promotes consensus under certain conditions. In disordered phase and when the exchange of information is spatially effectively limited, various faces of disorder are observed, including system states, where the signatures of self-organised criticality manifest themselves as scale-free probability distribution function of cluster sizes. Then increase of noise level leads system to disordered random state. The critical noise level above which histograms of opinion clusters' sizes loose their scale-free character increases with increase of the easy of information flow.},
  file = {/home/aleksandar/Dropbox/Zotero/Kowalska-Styczeń_Malarz/Kowalska-Styczeń_Malarz_2020_Noise induced unanimity and disorder in opinion formation.pdf;/home/aleksandar/Zotero/storage/8X45X8FL/article.html},
  journal = {PLOS ONE},
  language = {en},
  number = {7}
}

@article{kozitsinFormalModelsOpinion2020,
  title = {Formal Models of Opinion Formation and Their Application to Real Data: Evidence from Online Social Networks},
  shorttitle = {Formal Models of Opinion Formation and Their Application to Real Data},
  author = {Kozitsin, Ivan V.},
  year = {2020},
  month = oct,
  volume = {0},
  pages = {1--28},
  publisher = {{Routledge}},
  issn = {0022-250X},
  doi = {10.1080/0022250X.2020.1835894},
  abstract = {In this paper, we analyze data on the opinion dynamics of 1,660,927 users of an online social network using formal models of opinion formation. We have observed that moderate users have a tendency to follow the average opinion of their online friends, which we interpret as a presence of bounded confidence. Further, we have discovered that the probability of moving toward the average opinion goes down if the difference between it and the user's opinion is too large. Another interesting feature uncovered is that if a user's opinion and the average opinion of their online friends are very similar, the influence also decreases.},
  annotation = {\_eprint: https://doi.org/10.1080/0022250X.2020.1835894},
  journal = {The Journal of Mathematical Sociology},
  keywords = {bounded confidence models,French-DeGroot model,Opinion formation models},
  number = {0}
}

@article{kozitsinOpinionConvergenceKrasnoshchekov,
  title = {Opinion Convergence in the {{Krasnoshchekov}} Model},
  author = {Kozitsin, Ivan Vladimirovich and Belolipetskii, Alexander Alexeyevich},
  doi = {10.1080/0022250X.2018.1531398},
  abstract = {In this paper, a rigorous mathematical analysis of the Krasnoshchekov model is presented. We have shown that in case a community does not contain any group of people having zero resistance to interpersonal influence, which are moreover isolated from the pressure of the rest of community, the Krasnoshchekov opinion readjustment procedure can be reduced to the Friedkin\textendash Johnsen dynamics. In turn, if one repeats the Krasnoshchekov opinion updating rule, the corresponding dynamics forces individuals' opinions to converge eventually to some terminal opinions, which are a consensus under the same conditions as in the French\textendash Harary\textendash DeGroot dynamics. Otherwise, the Krasnoshchekov dynamics exhibits patterns, which are much closer to the behavior of electrons in the superconductivity state.},
  journal = {Journal of Mathematical Sociology}
}

@article{kozitsinOpinionConvergenceKrasnoshchekova,
  title = {Opinion Convergence in the {{Krasnoshchekov}} Model},
  author = {Kozitsin, Ivan Vladimirovich and Belolipetskii, Alexander Alexeyevich},
  doi = {10.1080/0022250X.2018.1531398},
  abstract = {In this paper, a rigorous mathematical analysis of the Krasnoshchekov model is presented. We have shown that in case a community does not contain any group of people having zero resistance to interpersonal influence, which are moreover isolated from the pressure of the rest of community, the Krasnoshchekov opinion readjustment procedure can be reduced to the Friedkin\textendash Johnsen dynamics. In turn, if one repeats the Krasnoshchekov opinion updating rule, the corresponding dynamics forces individuals' opinions to converge eventually to some terminal opinions, which are a consensus under the same conditions as in the French\textendash Harary\textendash DeGroot dynamics. Otherwise, the Krasnoshchekov dynamics exhibits patterns, which are much closer to the behavior of electrons in the superconductivity state.},
  journal = {Journal of Mathematical Sociology}
}

@article{kozitsinOpinionDynamicsOnline2020,
  title = {Opinion {{Dynamics}} of {{Online Social Network Users}}: {{A Micro}}-{{Level Analysis}}},
  shorttitle = {Opinion {{Dynamics}} of {{Online Social Network Users}}},
  author = {Kozitsin, Ivan V.},
  year = {2020},
  month = nov,
  abstract = {In this paper, we present an empirical study of the opinion dynamics of a large-scale sample of online social network users. Opinions of users are estimated based on their subscriptions to information sources and we analyze how friendship connections affect the dynamics of these estimations. Our analysis has revealed traces of both positive (assimilative) and negative (repulsive) influence in how users decide whether to change their opinions, while magnitudes of changed opinions appear to obey a positive influence. We also found that individuals with networks that include many ideologically opposite users are less prone to radicalization than those with homogenous ones. Our results indicate that the opinion dynamics of online social network users cannot be described solely by any concrete theoretical opinion formation model; rather, they include patterns of multiple models simultaneously.},
  archivePrefix = {arXiv},
  eprint = {2011.00864},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Kozitsin/Kozitsin_2020_Opinion Dynamics of Online Social Network Users.pdf;/home/aleksandar/Zotero/storage/TTFWIJ75/2011.html},
  journal = {arXiv:2011.00864 [cs, math]},
  keywords = {Computer Science - Social and Information Networks,Mathematics - Dynamical Systems},
  primaryClass = {cs, math}
}

@article{kozitsinOpinionDynamicsOnlinea,
  title = {Opinion {{Dynamics}} of {{Online Social Network Users}}: {{A Micro}}-{{Level Analysis}}},
  author = {Kozitsin, Ivan V},
  pages = {26},
  file = {/home/aleksandar/Zotero/storage/UIKFB9LC/Kozitsin - Opinion Dynamics of Online Social Network Users A.pdf},
  language = {en}
}

@article{kraemerRegularizedEstimationLargescale2009,
  title = {Regularized Estimation of Large-Scale Gene Regulatory Networks Using {{Gaussian}} Graphical Models},
  author = {Kraemer, N. and Schaefer, J. and Boulesteix, A.-L.},
  year = {2009},
  volume = {10},
  doi = {10.1186/1471-2105-10-384},
  journal = {BMC Bioinformatics},
  number = {384}
}

@article{krakauerInformationTheoryIndividuality2020,
  title = {The Information Theory of Individuality},
  author = {Krakauer, David and Bertschinger, Nils and Olbrich, Eckehard and Flack, Jessica C. and Ay, Nihat},
  year = {2020},
  month = jun,
  volume = {139},
  pages = {209--223},
  issn = {1611-7530},
  doi = {10.1007/s12064-020-00313-7},
  abstract = {Despite the near universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. Here, we propose that individuals are aggregates that preserve a measure of temporal integrity, i.e., ``propagate'' information from their past into their futures. We formalize this idea using information theory and graphical models. This mathematical formulation yields three principled and distinct forms of individuality\textemdash an organismal, a colonial, and a driven form\textemdash each of which varies in the degree of environmental dependence and inherited information. This approach can be thought of as a Gestalt approach to evolution where selection makes figure-ground (agent\textendash environment) distinctions using suitable information-theoretic lenses. A benefit of the approach is that it expands the scope of allowable individuals to include adaptive aggregations in systems that are multi-scale, highly distributed, and do not necessarily have physical boundaries such as cell walls or clonal somatic tissue. Such individuals might be visible to selection but hard to detect by observers without suitable measurement principles. The information theory of individuality allows for the identification of individuals at all levels of organization from molecular to cultural and provides a basis for testing assumptions about the natural scales of a system and argues for the importance of uncertainty reduction through coarse-graining in adaptive systems.},
  file = {/home/aleksandar/Dropbox/Zotero/Krakauer et al/Krakauer et al_2020_The information theory of individuality.pdf},
  journal = {Theory in Biosciences},
  language = {en},
  number = {2}
}

@article{krauseMissingDataCrosssectional2020,
  title = {Missing Data in Cross-Sectional Networks \textendash{} {{An}} Extensive Comparison of Missing Data Treatment Methods},
  author = {Krause, Robert W. and Huisman, Mark and Steglich, Christian and Snijders, Tom},
  year = {2020},
  month = jul,
  volume = {62},
  pages = {99--112},
  issn = {0378-8733},
  doi = {10.1016/j.socnet.2020.02.004},
  abstract = {This paper compares several missing data treatment methods for missing network data on a diverse set of simulated networks under several missing data mechanisms. We focus the comparison on three different outcomes: descriptive statistics, link reconstruction, and model parameters. The results indicate that the often used methods (analysis of available cases and null-tie imputation) lead to considerable bias on descriptive statistics with moderate or large proportions of missing data. Multiple imputation using sophisticated imputation models based on exponential random graph models (ERGMs) lead to acceptable biases in descriptive statistics and model parameters even under large amounts of missing data. For link reconstruction multiple imputation by simple ERGM performed well on small data sets, while missing data was more accurately imputed in larger data sets with multiple imputation by complex Bayesian ERGMs (BERGMs).},
  file = {/home/aleksandar/Zotero/storage/7NUSPFEL/S0378873320300137.html},
  journal = {Social Networks},
  language = {en}
}

@article{kriesiSupportRadicalParties2020,
  title = {Support for Radical Parties in {{Western Europe}}: {{Structural}} Conflicts and Political Dynamics},
  shorttitle = {Support for Radical Parties in {{Western Europe}}},
  author = {Kriesi, Hanspeter and {Schulte-Cloos}, Julia},
  year = {2020},
  month = jun,
  volume = {65},
  pages = {102138},
  issn = {0261-3794},
  doi = {10.1016/j.electstud.2020.102138},
  abstract = {Why is the populist radical left and right on the rise across western Europe? Integrating theories on changing socio-political conflict with arguments about crises of political representation, we contend that electoral support for radical right and radical left parties is rooted in two distinct sets of socio-structural factors, but their translation into electoral choice is in both cases conditioned by the individual political discontent that originates in specific political dynamics. Relying on the European Social Survey (ESS) covering the period from 2002 to 2016 and Parlgov data, we show that the lack of responsiveness of mainstream parties to the changing social conflict structure provides critical opportunities for new challengers from both the radical left and the radical right, while voters' political discontent only works to heighten their success when these parties are in opposition. Our article contributes not only by offering an integrative account of the electoral appeal of the radical right and radical left parties. In emphasising the largely similar nature of short-term, political factors that condition the translation of the different sets of long-term, structural determinants into opting for these parties, critically, this article also contributes to understanding the electoral success of radical challengers across western Europe.},
  journal = {Electoral Studies},
  language = {en}
}

@article{kriesiThereCrisisDemocracy2020,
  title = {Is {{There}} a~{{Crisis}} of {{Democracy}} in {{Europe}}?},
  author = {Kriesi, Hanspeter},
  year = {2020},
  month = mar,
  issn = {1862-2860},
  doi = {10.1007/s11615-020-00231-9},
  abstract = {This paper discusses the current prospects of democracy in Europe from four perspectives: the bird's-eye view of long-term trends; the perspective of citizens' support of democratic principles and their dissatisfaction with the way democracy works in their own countries; the voters' perspective, which points to the rise of populist challengers in reaction to rising democratic dissatisfaction; and the elites' perspective of populists in power. Overall, there is reason for concern, but no reason to dramatize. The long-term trends point to the expansion of democracy; the citizens' support for democracy is still massive in Europe. At the same time, democratic dissatisfaction is widespread, giving rise to the surge of populist challengers from the left and the right. However, even if they gain power, populists meet with a~large number of constraints that stabilize democracy.},
  journal = {Politische Vierteljahresschrift},
  language = {en}
}

@article{kriesiThereCrisisDemocracy2020a,
  title = {Is {{There}} a {{Crisis}} of {{Democracy}} in {{Europe}}?},
  author = {Kriesi, Hanspeter},
  year = {2020},
  month = jun,
  volume = {61},
  pages = {237--260},
  issn = {0032-3470, 1862-2860},
  doi = {10.1007/s11615-020-00231-9},
  abstract = {This paper discusses the current prospects of democracy in Europe from four perspectives: the bird's-eye view of long-term trends; the perspective of citizens' support of democratic principles and their dissatisfaction with the way democracy works in their own countries; the voters' perspective, which points to the rise of populist challengers in reaction to rising democratic dissatisfaction; and the elites' perspective of populists in power. Overall, there is reason for concern, but no reason to dramatize. The long-term trends point to the expansion of democracy; the citizens' support for democracy is still massive in Europe. At the same time, democratic dissatisfaction is widespread, giving rise to the surge of populist challengers from the left and the right. However, even if they gain power, populists meet with a large number of constraints that stabilize democracy.},
  file = {/home/aleksandar/Zotero/storage/4GFRBWDJ/Kriesi - 2020 - Is There a Crisis of Democracy in Europe.pdf},
  journal = {Politische Vierteljahresschrift},
  language = {en},
  number = {2}
}

@techreport{krrNonPharmaceuticalInterventionsNPIs2020,
  title = {Non-{{Pharmaceutical Interventions}} ({{NPIs}}) to {{Reduce COVID}}-19 {{Mortality}}},
  author = {Krr, Gandhi and Kvr, Murthy and Ssp, Prasada Rao and Casella, Francesco},
  year = {2020},
  month = mar,
  address = {{Rochester, NY}},
  institution = {{Social Science Research Network}},
  doi = {10.2139/ssrn.3560688},
  abstract = {The momentum episode of novel coronavirus infection 2019 (COVID-19) represents a remarkable worldwide wellbeing and monetary risk to interconnected human social orders. Until an antibody is created, systems for controlling the flare-up depend on forceful social removing. In this connection, We tended to the significance of NPI's based on R* value by grouping the nations. In light of R* values, we proposed the most ideal and NPI's for conceivable decrease of R* through CFR for the interest of all nations. In this manner, countries/regions can play it safe dependent on R* to diminish the spread of COVID-19.},
  file = {/home/aleksandar/Zotero/storage/X9I43K5H/papers.html},
  language = {en},
  number = {ID 3560688},
  type = {{{SSRN Scholarly Paper}}}
}

@article{kruisThreeRepresentationsIsing2016,
  title = {Three Representations of the {{Ising}} Model},
  author = {Kruis, Joost and Maris, Gunter},
  year = {2016},
  month = dec,
  volume = {6},
  issn = {2045-2322},
  doi = {10.1038/srep34175},
  journal = {Scientific Reports},
  language = {en},
  number = {1}
}

@article{kruisTransformationsMixedSpinclass2020,
  title = {Transformations of Mixed Spin-Class {{Ising}} Systems},
  author = {Kruis, Joost},
  year = {2020},
  month = jun,
  abstract = {In the many fields in which the Ising model is applied nowadays, the spin variables are often assumed to be of spin-class \$\textbackslash\{-1,1\textbackslash\}\$ or \$\textbackslash\{0,1\textbackslash\}\$, even though for any mix of binary real valued spin-classes a proper Ising model distribution exists. Here we show that in their basis all these spin-classes are the same, as a simple expressions exist that allow us to transform the variables from one particular mix of spin-classes to any other combination, without changing the probabilities for the system states as a whole.},
  archivePrefix = {arXiv},
  eprint = {2006.13581},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Kruis/Kruis_2020_Transformations of mixed spin-class Ising systems.pdf;/home/aleksandar/Zotero/storage/4B2VX24P/2006.html},
  journal = {arXiv:2006.13581 [cond-mat]},
  primaryClass = {cond-mat}
}

@techreport{kubinecGettingGoldStandard2020,
  title = {Getting {{Off}} the {{Gold Standard}} for {{Causal Inference}}},
  author = {Kubinec, Robert},
  year = {2020},
  month = feb,
  institution = {{SocArXiv}},
  doi = {10.31235/osf.io/a492b},
  abstract = {I argue that social scientific practice would be improved if we abandoned the notion that a gold standard for causal inference exists. Instead, we should consider the three main inference modes--experimental approaches and the counterfactual theory, large-N observational studies, and qualitative process-tracing--as observable indicators of an unobserved latent construct, causality. Under ideal conditions, any of these three approaches provide strong, though not perfect, evidence of an important part of what we mean by causality. I also present the use of statistical entropy, or information theory, as a possible yardstick for evaluating research designs across the silver standards. Rather than dichotomize studies as either causal or descriptive, the concept of relative entropy instead emphasizes the relative causal knowledge gained from a given research finding.},
  file = {/home/aleksandar/Dropbox/Zotero/Kubinec/Kubinec_2020_Getting Off the Gold Standard for Causal Inference.pdf},
  language = {en},
  type = {Preprint}
}

@article{kuehnMathematicalFrameworkCritical2013,
  title = {A {{Mathematical Framework}} for {{Critical Transitions}}: {{Normal Forms}}, {{Variance}} and {{Applications}}},
  shorttitle = {A {{Mathematical Framework}} for {{Critical Transitions}}},
  author = {Kuehn, Christian},
  year = {2013},
  month = jun,
  volume = {23},
  pages = {457--510},
  issn = {1432-1467},
  doi = {10.1007/s00332-012-9158-x},
  abstract = {Critical transitions occur in a wide variety of applications including mathematical biology, climate change, human physiology and economics. Therefore it is highly desirable to find early-warning signs. We show that it is possible to classify critical transitions by using bifurcation theory and normal forms in the singular limit. Based on this elementary classification, we analyze stochastic fluctuations and calculate scaling laws of the variance of stochastic sample paths near critical transitions for fast-subsystem bifurcations up to codimension two. The theory is applied to several models: the Stommel\textendash Cessi box model for the thermohaline circulation from geoscience, an epidemic-spreading model on an adaptive network, an activator\textendash inhibitor switch from systems biology, a predator\textendash prey system from ecology and to the Euler buckling problem from classical mechanics. For the Stommel\textendash Cessi model we compare different detrending techniques to calculate early-warning signs. In the epidemics model we show that link densities could be better variables for prediction than population densities. The activator\textendash inhibitor switch demonstrates effects in three time-scale systems and points out that excitable cells and molecular units have information for subthreshold prediction. In the predator\textendash prey model explosive population growth near a codimension-two bifurcation is investigated and we show that early-warnings from normal forms can be misleading in this context. In the biomechanical model we demonstrate that early-warning signs for buckling depend crucially on the control strategy near the instability which illustrates the effect of multiplicative noise.},
  file = {/home/aleksandar/Zotero/storage/U6W8N5DH/Kuehn - 2013 - A Mathematical Framework for Critical Transitions.pdf},
  journal = {Journal of Nonlinear Science},
  language = {en},
  number = {3}
}

@article{kuhaNonequivalenceMeasurementLatent2015,
  title = {Nonequivalence of Measurement in Latent Variable Modeling of Multigroup Data: {{A}} Sensitivity Analysis.},
  shorttitle = {Nonequivalence of Measurement in Latent Variable Modeling of Multigroup Data},
  author = {Kuha, Jouni and Moustaki, Irini},
  year = {2015},
  month = dec,
  volume = {20},
  pages = {523--536},
  issn = {1939-1463, 1082-989X},
  doi = {10.1037/met0000031},
  file = {/home/aleksandar/Dropbox/Zotero/Kuha_Moustaki/Kuha_Moustaki_2015_Nonequivalence of measurement in latent variable modeling of multigroup data.pdf},
  journal = {Psychological Methods},
  language = {en},
  number = {4}
}

@article{kumarCommunityDetectionComplex2020,
  title = {Community Detection in Complex Networks Using Network Embedding and Gravitational Search Algorithm},
  author = {Kumar, Sanjay and Panda, B. S. and Aggarwal, Deepanshu},
  year = {2020},
  month = nov,
  issn = {1573-7675},
  doi = {10.1007/s10844-020-00625-6},
  abstract = {The structural and functional characteristic features of nodes can be analyzed by visualizing community structure in complex networks. Community detection helps us to detect nodes having similar behavior in a system and organize the network into a network of closely connected groups or modules. Network embedding technique represents the nodes of the input graph into vector space and preserves their inherent and topological features and can contribute significantly to various applications in network analysis. In this paper, we propose a novel community detection method using network embedding technique. Firstly, nodes of the graph are embedded in feature space of d dimensions, and then low-rank approximation is applied to avoid the results from being affected by noise or outliers. Further, k-means clustering is employed to find the centroids of the clusters in the network and followed by a gravitational search algorithm to improve the results of centroids of clusters. Finally, we compute the effectiveness of detected communities using different performance measures. Our method serves as a universal framework towards applying and bench-marking various embedding techniques in graphs for performing community detection. We perform the test using various evaluation criteria on several real-life and synthetic networks and the obtained result reveals the utility of the proposed algorithm.},
  file = {/home/aleksandar/Documents/Zotero/Kumar et al/Kumar et al_2020_Community detection in complex networks using network embedding and.pdf},
  journal = {Journal of Intelligent Information Systems},
  language = {en}
}

@article{kumarCommunityDetectionComplex2020a,
  title = {Community Detection in Complex Networks Using Network Embedding and Gravitational Search Algorithm},
  author = {Kumar, Sanjay and Panda, B. S. and Aggarwal, Deepanshu},
  year = {2020},
  month = nov,
  issn = {1573-7675},
  doi = {10.1007/s10844-020-00625-6},
  abstract = {The structural and functional characteristic features of nodes can be analyzed by visualizing community structure in complex networks. Community detection helps us to detect nodes having similar behavior in a system and organize the network into a network of closely connected groups or modules. Network embedding technique represents the nodes of the input graph into vector space and preserves their inherent and topological features and can contribute significantly to various applications in network analysis. In this paper, we propose a novel community detection method using network embedding technique. Firstly, nodes of the graph are embedded in feature space of d dimensions, and then low-rank approximation is applied to avoid the results from being affected by noise or outliers. Further, k-means clustering is employed to find the centroids of the clusters in the network and followed by a gravitational search algorithm to improve the results of centroids of clusters. Finally, we compute the effectiveness of detected communities using different performance measures. Our method serves as a universal framework towards applying and bench-marking various embedding techniques in graphs for performing community detection. We perform the test using various evaluation criteria on several real-life and synthetic networks and the obtained result reveals the utility of the proposed algorithm.},
  journal = {Journal of Intelligent Information Systems},
  language = {en}
}

@article{kumarFormalisingDetectingCommunity2020,
  title = {Formalising and {{Detecting Community Structures}} in {{Real World Complex Networks}}},
  author = {Kumar, Pawan and Dohare, Ravins},
  year = {2020},
  month = nov,
  issn = {1559-7067},
  doi = {10.1007/s11424-020-9252-3},
  abstract = {Community structure is an integral characteristic of real world networks whichever processes or areas they emerge from. This paper addresses the problem of community structure detection theoretically as well as computationally. The authors introduce a number of concepts such as the neighbourhood and strength of a subgraph, p-community, local maximal p-community, hubs, and outliers that play elemental role in formalising the concept of community structure in complex networks. A few preliminary results have been derived that lead to the development of an algorithm for community structure detection in undirected unweighted networks. The algorithm is based on a local seed expansion strategy that uses the concept of interaction coefficient. The authors have analysed the algorithm on a number of parameters such as accuracy, stability, and quality on synthetic and real world networks from different areas.},
  file = {/home/aleksandar/Documents/Zotero/Kumar_Dohare/Kumar_Dohare_2020_Formalising and Detecting Community Structures in Real World Complex Networks.pdf},
  journal = {Journal of Systems Science and Complexity},
  language = {en}
}

@article{kurantExtractionAnalysisTraffic2006,
  title = {Extraction and Analysis of Traffic and Topologies of Transportation Networks},
  author = {Kurant, Maciej and Thiran, Patrick},
  year = {2006},
  month = sep,
  volume = {74},
  issn = {1539-3755, 1550-2376},
  doi = {10.1103/PhysRevE.74.036114},
  journal = {Physical Review E},
  language = {en},
  number = {3}
}

@article{kuterSUNNYNewAlgorithm,
  title = {{{SUNNY}}: {{A New Algorithm}} for {{Trust Inference}} in {{Social Networks Using Probabilistic Confidence Models}}},
  author = {Kuter, Ugur},
  pages = {6},
  abstract = {In many computing systems, information is produced and processed by many people. Knowing how much a user trusts a source can be very useful for aggregating, filtering, and ordering of information. Furthermore, if trust is used to support decision making, it is important to have an accurate estimate of trust when it is not directly available, as well as a measure of confidence in that estimate. This paper describes a new approach that gives an explicit probabilistic interpretation for confidence in social networks. We describe SUNNY, a new trust inference algorithm that uses a probabilistic sampling technique to estimate our confidence in the trust information from some designated sources. SUNNY computes an estimate of trust based on only those information sources with high confidence estimates. In our experiments, SUNNY produced more accurate trust estimates than the well known trust inference algorithm TIDALTRUST (Golbeck 2005), demonstrating its effectiveness.},
  file = {/home/aleksandar/Zotero/storage/VD72CFJP/Kuter - SUNNY A New Algorithm for Trust Inference in Soci.pdf},
  language = {en}
}

@article{kutnerEconophysicsSociophysicsTheir,
  title = {Econophysics and Sociophysics: {{Their}} Milestones \& Challenges},
  author = {Kutner, Ryszard and Ausloos, Marcel and Grech, Dariusz and Matteo, Tiziana Di and Schinckus, Christophe and Stanley, H. Eugene},
  doi = {S0378437118313578},
  abstract = {{$<$}p{$>$}Publication date: 15 February 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications, Volume 516{$<$}/p{$><$}p{$>$}Author(s): Ryszard Kutner, Marcel Ausloos, Dariusz Grech, Tiziana Di Matteo, Christophe Schinckus, H. Eugene Stanley{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}In this review article we present some of achievements of econophysics and sociophysics which appear to us the most significant. We briefly explain what their roles are in building of econo- and sociophysics research fields. We point to milestones of econophysics and sociophysics facing to challenges and open problems.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{kutnerEconophysicsSociophysicsTheira,
  title = {Econophysics and Sociophysics: {{Their}} Milestones \& Challenges},
  author = {Kutner, Ryszard and Ausloos, Marcel and Grech, Dariusz and Matteo, Tiziana Di and Schinckus, Christophe and Stanley, H. Eugene},
  doi = {S0378437118313578},
  abstract = {{$<$}p{$>$}Publication date: 15 February 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications, Volume 516{$<$}/p{$><$}p{$>$}Author(s): Ryszard Kutner, Marcel Ausloos, Dariusz Grech, Tiziana Di Matteo, Christophe Schinckus, H. Eugene Stanley{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}In this review article we present some of achievements of econophysics and sociophysics which appear to us the most significant. We briefly explain what their roles are in building of econo- and sociophysics research fields. We point to milestones of econophysics and sociophysics facing to challenges and open problems.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{laaksoEffectiveNumberParties1979,
  title = {``{{Effective}}'' Number of Parties: {{A}} Measure with Application to {{West Europe}}},
  shorttitle = {``{{Effective}}'' {{Number}} of {{Parties}}},
  author = {Laakso, Markku and Taagepera, Rein},
  year = {1979},
  month = apr,
  volume = {12},
  pages = {3--27},
  issn = {0010-4140, 1552-3829},
  doi = {10.1177/001041407901200101},
  journal = {Comparative Political Studies},
  language = {en},
  number = {1}
}

@misc{LabelPropagationAlgorithm,
  title = {A {{Label Propagation Algorithm}} Based on {{Similarity}} and {{Weighted}} - {{IEEE Conference Publication}}},
  file = {/home/aleksandar/Zotero/storage/WKFWGVCD/9141630.html},
  howpublished = {https://ieeexplore.ieee.org/abstract/document/9141630}
}

@unpublished{lacyWhyDonStates2017,
  title = {Why Don't States Switch Sides Anymore? {{The}} Rise and Fall of {{American}} Electoral Volatility},
  author = {Lacy, D. and Markovich, Z.D.},
  year = {2017}
}

@article{lafitPartialCorrelationScreening2019,
  title = {A {{Partial Correlation Screening Approach}} for {{Controlling}} the {{False Positive Rate}} in {{Sparse Gaussian Graphical Models}}},
  author = {Lafit, Ginette and Tuerlinckx, Francis and {Myin-Germeys}, Inez and Ceulemans, Eva},
  year = {2019},
  month = nov,
  volume = {9},
  pages = {1--24},
  issn = {2045-2322},
  doi = {10.1038/s41598-019-53795-x},
  abstract = {Gaussian Graphical Models (GGMs) are extensively used in many research areas, such as genomics, proteomics, neuroimaging, and psychology, to study the partial correlation structure of a set of variables. This structure is visualized by drawing an undirected network, in which the variables constitute the nodes and the partial correlations the edges. In many applications, it makes sense to impose sparsity (i.e., some of the partial correlations are forced to zero) as sparsity is theoretically meaningful and/or because it improves the predictive accuracy of the fitted model. However, as we will show by means of extensive simulations, state-of-the-art estimation approaches for imposing sparsity on GGMs, such as the Graphical lasso, {$\mathscr{l}$}1 regularized nodewise regression, and joint sparse regression, fall short because they often yield too many false positives (i.e., partial correlations that are not properly set to zero). In this paper we present a new estimation approach that allows to control the false positive rate better. Our approach consists of two steps: First, we estimate an undirected network using one of the three state-of-the-art estimation approaches. Second, we try to detect the false positives, by flagging the partial correlations that are smaller in absolute value than a given threshold, which is determined through cross-validation; the flagged correlations are set to zero. Applying this new approach to the same simulated data, shows that it indeed performs better. We also illustrate our approach by using it to estimate (1) a gene regulatory network for breast cancer data, (2) a symptom network of patients with a diagnosis within the nonaffective psychotic spectrum and (3) a symptom network of patients with PTSD.},
  copyright = {2019 The Author(s)},
  journal = {Scientific Reports},
  language = {en},
  number = {1}
}

@article{lagoDefiningMeasuringParty2014,
  title = {Defining and Measuring Party System Nationalization},
  author = {Lago, Ignacio and Montero, Jos{\'e} Ram{\'o}n},
  year = {2014},
  month = may,
  volume = {6},
  pages = {191--211},
  issn = {1755-7739, 1755-7747},
  doi = {10.1017/S1755773913000027},
  abstract = {In this paper, we propose a new measure of party system nationalization based on a minimalist definition of the phenomenon. A perfectly nationalized party system is a party system with only national parties or, in other words, without sub-national parties. Instead of the homogeneity of parties' vote shares or the number of parties throughout the country, our measure captures the aspirations of parties to be national whose proxy is the proportion of districts (weighted by seats) that a party runs a candidate. The measure is compared with existing indicators through a longitudinal analysis of 256 elections in 18 Western European countries from 1945 to 1998.},
  journal = {European Political Science Review},
  language = {en},
  number = {2}
}

@article{lagoResearchAgendaElections2019,
  title = {A {{Research Agenda}} in {{Elections}} and {{Voting Behavior}} in a {{Global}} and {{Changing World}}},
  author = {Lago, Ignacio},
  year = {2019},
  volume = {1},
  issn = {2673-3145},
  doi = {10.3389/fpos.2019.00001},
  abstract = {A Research Agenda in Elections and Voting Behavior in a Global and Changing World},
  journal = {Frontiers in Political Science},
  language = {English}
}

@inproceedings{lahiriTODDTimeAwareOpinion2021,
  title = {{{TODD}}: {{Time}}-{{Aware Opinion Dynamics Diffusion Model}} for {{Online Social Networks}}},
  shorttitle = {{{TODD}}},
  booktitle = {Proceedings of {{International Conference}} on {{Artificial Intelligence}} and {{Applications}}},
  author = {Lahiri, Aditya and Singhal, Yash Kumar and Sinha, Adwitiya},
  editor = {Bansal, Poonam and Tushir, Meena and Balas, Valentina  Emilia and Srivastava, Rajeev},
  year = {2021},
  pages = {235--245},
  publisher = {{Springer}},
  address = {{Singapore}},
  doi = {10.1007/978-981-15-4992-2_23},
  abstract = {Information diffusion is employed to track the spread of information in the online social network, and hence, is considered extremely relevant to several modern-day applications. It is of pivotal importance for addressing challenges associated with the influence maximization in social media. Our research focuses on the diffusion process to track the temporal extent of information spread in the network. Over time, various models have been suggested in order to best represent the diffusion process. Our paper suggests a novel information diffusion model, time-aware opinion dynamics model (TODD), that derives from opinion dynamics and continuous time models in order to provide a more robust and realistic representation of the real-world problem. A comparative study is conducted between our proposed model and several existing methods.},
  isbn = {9789811549922},
  language = {en},
  series = {Advances in {{Intelligent Systems}} and {{Computing}}}
}

@article{laiSocialDynamicsParrondo2020,
  title = {Social Dynamics and {{Parrondo}}'s Paradox: A Narrative Review},
  shorttitle = {Social Dynamics and {{Parrondo}}'s Paradox},
  author = {Lai, Joel Weijia and Cheong, Kang Hao},
  year = {2020},
  month = jun,
  issn = {1573-269X},
  doi = {10.1007/s11071-020-05738-9},
  abstract = {How do group dynamics affect individuals within the group? How do individuals, in turn, affect group dynamics? As society comes together, individuals affect the group dynamics and vice versa. Social dynamics look at group dynamics, its effect on individuals, conformity, leadership, networks, and more. In the past two decades, the game theoretic Parrondo's paradox has been used to model and explain the different aspects of social dynamics. Two losing games can be combined in a certain manner to give a winning outcome\textemdash this is known as Parrondo's paradox. In this review, the connections between Parrondo's paradox and social dynamics are discussed with emphasis on (i) cooperation and competition, (ii) resource redistribution and social welfare, and (iii) information flow and decision-making.},
  file = {/home/aleksandar/Dropbox/Zotero/Lai_Cheong/Lai_Cheong_2020_Social dynamics and Parrondo’s paradox.pdf},
  journal = {Nonlinear Dynamics},
  language = {en}
}

@article{lanchierAxelrodModelDissemination2012,
  title = {The {{Axelrod}} Model for the Dissemination of Culture Revisited},
  author = {Lanchier, Nicolas},
  year = {2012},
  month = apr,
  volume = {22},
  pages = {860--880},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {1050-5164, 2168-8737},
  doi = {10.1214/11-AAP790},
  abstract = {This article is concerned with the Axelrod model, a stochastic process which similarly to the voter model includes social influence, but unlike the voter model also accounts for homophily. Each vertex of the network of interactions is characterized by a set of F cultural features, each of which can assume q states. Pairs of adjacent vertices interact at a rate proportional to the number of features they share, which results in the interacting pair having one more cultural feature in common. The Axelrod model has been extensively studied during the past ten years, based on numerical simulations and simple mean-field treatments, while there is a total lack of analytical results for the spatial model itself. Simulation results for the one-dimensional system led physicists to formulate the following conjectures. When the number of features F and the number of states q both equal two, or when the number of features exceeds the number of states, the system converges to a monocultural equilibrium in the sense that the number of cultural domains rescaled by the population size converges to zero as the population goes to infinity. In contrast, when the number of states exceeds the number of features, the system freezes in a highly fragmented configuration in which the ultimate number of cultural domains scales like the population size. In this article, we prove analytically for the one-dimensional system convergence to a monocultural equilibrium in terms of clustering when F = q = 2, as well as fixation to a highly fragmented configuration when the number of states is sufficiently larger than the number of features. Our first result also implies clustering of the one-dimensional constrained voter model.},
  file = {/home/aleksandar/Documents/Zotero/Lanchier/Lanchier_2012_The Axelrod model for the dissemination of culture revisited.pdf;/home/aleksandar/Zotero/storage/IGTR7C7H/1333372015.html},
  journal = {Annals of Applied Probability},
  language = {EN},
  mrnumber = {MR2953571},
  number = {2},
  zmnumber = {1245.60095}
}

@article{lanchierProbabilityConsensusMultivariate2020,
  title = {Probability of Consensus in the Multivariate {{Deffuant}} Model on Finite Connected Graphs},
  author = {Lanchier, Nicolas and Li, Hsin-Lun},
  year = {2020},
  month = may,
  abstract = {The Deffuant model is a spatial stochastic model for the dynamics of opinions in which individuals are located on a connected graph representing a social network and characterized by a number in the unit interval representing their opinion. The system evolves according to the following averaging procedure: pairs of neighbors interact independently at rate one if and only if the distance between their opinions does not exceed a certain confidence threshold, with each interaction resulting in the neighbors' opinions getting closer to each other. All the mathematical results collected so far about this model assume that the individuals are located on the integers. In contrast, we study the more realistic case where the social network can be any finite connected graph. In addition, we extend the opinion space to any bounded convex subset of a normed vector space where the norm is used to measure the level of disagreement or distance between the opinions. Our main result gives a lower bound for the probability of consensus. Interestingly, our proof leads to a universal lower bound that depends on the confidence threshold, the opinion space\textasciitilde (convex subset and norm) and the initial distribution, but not on the size or the topology of the social network.},
  archivePrefix = {arXiv},
  eprint = {2005.13141},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Lanchier_Li/Lanchier_Li_2020_Probability of consensus in the multivariate Deffuant model on finite connected.pdf;/home/aleksandar/Dropbox/research/papers/lanchierProbabilityConsensusMultivariate2020.md},
  journal = {arXiv:2005.13141 [math]},
  primaryClass = {math}
}

@article{landryEffectHeterogeneityHypergraph2020,
  title = {The Effect of Heterogeneity on Hypergraph Contagion Models},
  author = {Landry, Nicholas and Restrepo, Juan G.},
  year = {2020},
  month = jun,
  abstract = {The dynamics of network social contagion processes such as opinion formation and epidemic spreading is often mediated by interactions between multiple nodes. Previous results have shown that these higher-order interactions can profoundly modify the dynamics of contagion processes, resulting in bistability, hysteresis, and explosive transitions. In this paper, we present and analyze a degree-based mean-field description of the dynamics of the SIS model on hypergraphs, i.e., networks with higher-order interactions. We derive a hyperdegree-based mean-field equation to describe the contagion dynamics, and illustrate its applicability with the example of a hypergraph where contagion is mediated by both links (pairwise interactions) and triangles (three-way interactions). We consider two different mechanisms of higher-order contagion and healing, and the cases where links and triangles connect preferentially to the same nodes, or are chosen independently of each other. We find that, when links and triangles are chosen independently, heterogeneity in the link degree distribution can suppress explosive transitions. In addition, explosive transitions are more likely to occur when node and triangle connections are positively correlated when compared to the case when they are chosen independently of each other. We verify these results with microscopic simulations of the contagion process and with analytic predictions derived from the mean-field model. Our results show that the structure of higher-order interactions can have important effects on contagion processes on hypergraphs.},
  archivePrefix = {arXiv},
  eprint = {2006.15453},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Landry_Restrepo/Landry_Restrepo_2020_The effect of heterogeneity on hypergraph contagion models.pdf;/home/aleksandar/Zotero/storage/NSQJ8TDM/2006.html},
  journal = {arXiv:2006.15453 [physics]},
  primaryClass = {physics}
}

@article{lanePartySystemInstability2007,
  title = {Party System Instability in {{Europe}}: {{Persistent}} Differences in Volatility between {{West}} and {{East}}?},
  shorttitle = {Party {{System Instability}} in {{Europe}}},
  author = {Lane, Jan-Erik and Ersson, Svante},
  year = {2007},
  month = feb,
  volume = {14},
  pages = {92--110},
  issn = {1351-0347, 1743-890X},
  doi = {10.1080/13510340601024322},
  journal = {Democratization},
  language = {en},
  number = {1}
}

@techreport{langeIntegrativePsychometricModel2019,
  ids = {langeIntegrativePsychometricModel2019a},
  title = {Toward an {{Integrative Psychometric Model}} of {{Emotions}}},
  author = {Lange, Jens and Dalege, Jonas and Borsboom, Denny and {van Kleef}, Gerben and Fischer, Agneta},
  year = {2019},
  month = oct,
  institution = {{Open Science Framework}},
  doi = {10.31219/osf.io/7ysjk},
  abstract = {Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect program theories, constructionist theories, and appraisal theories. Integrating them in a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory, because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we (a) delineate properties of emotions stated by emotion theories, and (b) investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow identifying distinct emotions (central in affect program theories), should allow between and within person variation of emotions (central in constructionist theories), and should allow causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models\textemdash in which emotions are conceptualized as unobservable causes or consequences of emotion components\textemdash cannot account for all properties. Conversely, a psychometric network model\textemdash in which emotions are conceptualized as systems of causally interacting emotion components\textemdash accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.},
  type = {Preprint}
}

@article{lartigueGaussianGraphicalModel2020,
  title = {Gaussian {{Graphical Model}} Exploration and Selection in High Dimension Low Sample Size Setting},
  author = {Lartigue, Thomas and Bottani, Simona and Baron, Stephanie and Colliot, Olivier and Durrleman, Stanley and Allassonni{\`e}re, St{\'e}phanie},
  year = {2020},
  month = mar,
  abstract = {Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: the nodewise approach of [1] and [2] and the penalised likelihood maximisation of [3] and [4]. We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one. As a result, we propose a composite procedure that explores a family of graphs with a nodewise numerical scheme and selects a candidate among them with an overall likelihood criterion. We demonstrate that, when the number of observations is small, this selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence with regards to the real distribution than the other two. Finally, we show the interest of our algorithm on two concrete cases: first on brain imaging data, then on biological nephrology data. In both cases our results are more in line with current knowledge in each field.},
  archivePrefix = {arXiv},
  eprint = {2003.05169},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Lartigue et al/Lartigue et al_2020_Gaussian Graphical Model exploration and selection in high dimension low sample.pdf},
  journal = {arXiv:2003.05169 [cs, stat]},
  language = {en},
  primaryClass = {cs, stat}
}

@article{laszkiewiczThresholdedAdaptiveValidation2020,
  title = {Thresholded {{Adaptive Validation}}: {{Tuning}} the {{Graphical Lasso}} for {{Graph Recovery}}},
  shorttitle = {Thresholded {{Adaptive Validation}}},
  author = {Laszkiewicz, Mike and Fischer, Asja and Lederer, Johannes},
  year = {2020},
  month = may,
  abstract = {The graphical lasso is the most popular estimator in Gaussian graphical models, but its performance hinges on a regularization parameter that needs to be calibrated to each application at hand. In this paper, we propose a novel calibration scheme for this parameter. The scheme is equipped with theoretical guarantees and motivates a thresholding pipeline that can improve graph recovery. Moreover, requiring at most one line search over the regularization path of the graphical lasso, the calibration scheme is computationally more efficient than competing schemes that are based on resampling. Finally, we show in simulations that our approach can improve on the graph recovery of other approaches considerably.},
  archivePrefix = {arXiv},
  eprint = {2005.00466},
  eprinttype = {arxiv},
  journal = {arXiv:2005.00466 [cs, stat]},
  language = {en},
  primaryClass = {cs, stat}
}

@book{lauritzenGraphicalModels1996a,
  title = {Graphical Models},
  author = {Lauritzen, Steffen L.},
  year = {1996},
  publisher = {{Clarendon Press ; Oxford University Press}},
  address = {{Oxford : New York}},
  isbn = {978-0-19-852219-5},
  lccn = {QA279 .L35 1996},
  number = {17},
  series = {Oxford Statistical Science Series}
}

@article{laycraftDecisionMakingSelfOrganizingProcess2019,
  title = {Decision-{{Making}} as a {{Self}}-{{Organizing Process}}},
  author = {Laycraft, Krystyna C},
  year = {2019},
  pages = {15},
  abstract = {In this article, we present the analysis of the process of decision-making by applying the main concepts of complexity science linked with the theories of emotions. Decision-making in our personal or immediate social life concerns our future and involves surprises, uncertainty, and complexity. This is why complexity science, and especially the idea of self-organization is the most suitable for studying this unpredictable and surprising phenomenon. We propose that the process of decision-making in human life goes through three phases: The will phase, the choice phase, and the decision phase. Primary emotions of surprise and anticipation and self-organizing patterns of emotions such as curiosity, attraction, doubt, optimism, resourcefulness, open-mindedness, enjoyment of unknown, and enthusiasm play a significant role in this process. In the discussion, the process of decision-making from the perspective of metacognition is included.},
  language = {en}
}

@article{lazegaEmbarkedSocialProcesses2020,
  title = {Embarked on Social Processes (the Rivers) in Dynamic and Multilevel Networks (the Boats)},
  author = {Lazega, Emmanuel},
  year = {2020},
  volume = {40},
  pages = {60--76},
  issn = {0226-1766},
  doi = {10.21307/connections-2019.013},
  abstract = {This paper is the written text underlying the keynote presentation at the Sunbelt XXXVIII in Utrecht, 2018. It presents a neo-structural approach to social processes in the organizational society and the usefulness of the analyses of multilevel networks to understand how we navigate these processes and are made aware of them when we face cooperation dilemmas. Empirical illustrations look at how multilevel networks and relational infrastructures are useful to research a process such as coopetitive learning in science, business and government. A con\-clusion focuses on the role of multilevel relational infrastructures in institutional entrepreneurship, social change and politics, as well as on our responsibility to develop our knowledge of these social processes and multilevel relational infrastructures as open science.},
  file = {/home/aleksandar/Dropbox/Zotero/Lazega/Lazega_2020_Embarked on social processes (the rivers) in dynamic and multilevel networks.pdf},
  journal = {Connections},
  language = {en},
  number = {1}
}

@article{lazerComputationalSocialScience,
  title = {Computational Social Science: {{Obstacles}} and Opportunities},
  author = {Lazer, David M J and Pentland, Alex and Watts, Duncan J and Aral, Sinan and Contractor, Noshir and Freelon, Deen and {Gonzalez-Bailon}, Sandra and King, Gary and Nelson, Alondra and Salganik, J and Strohmaier, Markus and Vespignani, Alessandro and Wagner, Claudia},
  pages = {4},
  file = {/home/aleksandar/Zotero/storage/2ZNFEBGW/Lazer et al. - Computational social science Obstacles and opport.pdf},
  language = {en}
}

@article{lazicClassValuesPostsocialist2007,
  title = {Class and {{Values}} in {{Postsocialist Transformation}} in {{Serbia}}},
  author = {Lazi{\'c}, Mladen and Cveji{\'c}, Slobodan},
  year = {2007},
  month = oct,
  volume = {37},
  pages = {54--74},
  issn = {0020-7659},
  doi = {10.2753/IJS0020-7659370303},
  abstract = {Circumstances such as the blocked transition in Serbia during the 1990s\textemdash with the prolonged political and economic domination of former nomenklatura members, the economic collapse of the country and massive pauperization of the population, external isolation, and so on\textemdash may result in the perseverance of old values among many social groups whose position has thus been extremely worsened. In this case, a gap between new norms and old values may be created, which we call value/normative dissonance. This gap is likely to cause a strong inter- and intraclass value inconsistency. In this article, we analyze the spread of dominant value orientations among social classes in the present postsocialist system in Serbia. The analysis is based on the data obtained in the South-East European Social Survey Project (SEESSP) in which 2,997 respondents participated in December 2003/January 2004. We analyze the spread of two pairs of conflicting value orientations: (1) political liberalism vs. authoritarian collectivism, and (2) market liberalism vs. redistributive statism. We also check intraclass value consistency. The influence of possible normative/value dissonance upon the democratization and market transformation of Serbia is evaluated. We compare the results of our analysis with the findings of an analysis of data obtained in an earlier survey, Change in Class Structure and Mobility in Serbia, in which 3,660 respondents participated in 1989. Our decision to deal with the two value orientations mentioned above was based on a factor analysis of a number of statements hypothetically related to different value systems and was confirmed by principal components analysis (PCA). Based on these results, we form scales to represent political and market liberalism, on one side, and authoritarian, redistributive statism, on the other. In the third step, we use analysis of variance (ANOVA) of the scale scores to describe value consistency among and within the social classes.},
  journal = {International Journal of Sociology},
  number = {3}
}

@article{leeChangingAttitudesSameSex2019,
  title = {Changing {{Attitudes Toward Same}}-{{Sex Marriage}}: {{A Three}}-{{Wave Panel Study}}},
  shorttitle = {Changing {{Attitudes Toward Same}}-{{Sex Marriage}}},
  author = {Lee, Hye-Yon and Mutz, Diana C.},
  year = {2019},
  month = sep,
  volume = {41},
  pages = {701--722},
  issn = {0190-9320, 1573-6687},
  doi = {10.1007/s11109-018-9463-7},
  journal = {Political Behavior},
  language = {en},
  number = {3}
}

@article{leeMeasuringGeographicDistribution,
  title = {Measuring {{Geographic Distribution}} for {{Political Research}}},
  author = {Lee, Dong Wook and Rogers, Melissa},
  doi = {10.1017/pan.2019.14},
  abstract = {Political scientists are increasingly interested in the geographic distribution of political and economic phenomena. Unlike distribution measures at the individual level, geographic distributions depend on the "unit question" in which researchers choose the appropriate political subdivision to analyze, such as nations, subnational regions, urban and rural areas, or electoral districts. We identify concerns with measuring geographic distribution and comparing distributions within and across political units. In particular, we highlight the potential for threats to inference based on the modifiable areal unit problem (MAUP), whereby measuring concepts at different unit aggregations alters the observed value. We offer tangible options for researchers to improve their research design and data analysis to limit the MAUP. To help manage the measurement error when the unit of observation is unclear or appropriate data are not available, we introduce a new measure of geographic distribution that accounts for fluctuations in the scale and number of political units considered. We demonstrate using Monte Carlo simulations that our measure is more reliable and stable across political units than commonly used indicators because it reduces measurement fluctuations associated with the MAUP.},
  journal = {Political Analysis}
}

@article{leeMeasuringGeographicDistributiona,
  title = {Measuring {{Geographic Distribution}} for {{Political Research}}},
  author = {Lee, Dong Wook and Rogers, Melissa},
  doi = {10.1017/pan.2019.14},
  abstract = {Political scientists are increasingly interested in the geographic distribution of political and economic phenomena. Unlike distribution measures at the individual level, geographic distributions depend on the "unit question" in which researchers choose the appropriate political subdivision to analyze, such as nations, subnational regions, urban and rural areas, or electoral districts. We identify concerns with measuring geographic distribution and comparing distributions within and across political units. In particular, we highlight the potential for threats to inference based on the modifiable areal unit problem (MAUP), whereby measuring concepts at different unit aggregations alters the observed value. We offer tangible options for researchers to improve their research design and data analysis to limit the MAUP. To help manage the measurement error when the unit of observation is unclear or appropriate data are not available, we introduce a new measure of geographic distribution that accounts for fluctuations in the scale and number of political units considered. We demonstrate using Monte Carlo simulations that our measure is more reliable and stable across political units than commonly used indicators because it reduces measurement fluctuations associated with the MAUP.},
  journal = {Political Analysis}
}

@article{leeMeasuringGeographicDistributionb,
  title = {Measuring {{Geographic Distribution}} for {{Political Research}}},
  author = {Lee, Dong Wook and Rogers, Melissa},
  doi = {10.1017/pan.2019.14},
  abstract = {Political scientists are increasingly interested in the geographic distribution of political and economic phenomena. Unlike distribution measures at the individual level, geographic distributions depend on the "unit question" in which researchers choose the appropriate political subdivision to analyze, such as nations, subnational regions, urban and rural areas, or electoral districts. We identify concerns with measuring geographic distribution and comparing distributions within and across political units. In particular, we highlight the potential for threats to inference based on the modifiable areal unit problem (MAUP), whereby measuring concepts at different unit aggregations alters the observed value. We offer tangible options for researchers to improve their research design and data analysis to limit the MAUP. To help manage the measurement error when the unit of observation is unclear or appropriate data are not available, we introduce a new measure of geographic distribution that accounts for fluctuations in the scale and number of political units considered. We demonstrate using Monte Carlo simulations that our measure is more reliable and stable across political units than commonly used indicators because it reduces measurement fluctuations associated with the MAUP.},
  journal = {Political Analysis}
}

@article{leeperMeasuringSubgroupPreferencesundefined/ed,
  title = {Measuring {{Subgroup Preferences}} in {{Conjoint Experiments}}},
  author = {Leeper, Thomas J. and Hobolt, Sara B. and Tilley, James},
  year = {undefined/ed},
  pages = {1--15},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2019.30},
  abstract = {Conjoint analysis is a common tool for studying political preferences. The method disentangles patterns in respondents' favorability toward complex, multidimensional objects, such as candidates or policies. Most conjoints rely upon a fully randomized design to generate average marginal component effects (AMCEs). They measure the degree to which a given value of a conjoint profile feature increases, or decreases, respondents' support for the overall profile relative to a baseline, averaging across all respondents and other features. While the AMCE has a clear causal interpretation (about the effect of features), most published conjoint analyses also use AMCEs to describe levels of favorability. This often means comparing AMCEs among respondent subgroups. We show that using conditional AMCEs to describe the degree of subgroup agreement can be misleading as regression interactions are sensitive to the reference category used in the analysis. This leads to inferences about subgroup differences in preferences that have arbitrary sign, size, and significance. We demonstrate the problem using examples drawn from published articles and provide suggestions for improved reporting and interpretation using marginal means and an omnibus F-test. Given the accelerating use of these designs in political science, we offer advice for best practice in analysis and presentation of results.},
  file = {/home/aleksandar/Zotero/storage/TDZW8KJU/4F2C21AC02753F1FFF2F5EA0F943C1B2.html},
  journal = {Political Analysis},
  language = {en}
}

@article{leeUncoveringHiddenDependency2020,
  title = {Uncovering {{Hidden Dependency}} in {{Weighted Networks}} via {{Information Entropy}}},
  author = {Lee, Mi Jin and Lee, Eun and Lee, Byunghwee and Jeong, Hawoong and Lee, Deok-Sun and Lee, Sang Hoon},
  year = {2020},
  month = aug,
  abstract = {Interactions between elements, which are usually represented by networks, have to delineate potentially unequal relationships in terms of their relative importance or direction. The intrinsic unequal relationships of such kind, however, are opaque or hidden in numerous real systems. For instance, when a node in a network with limited interaction capacity spends its capacity to its neighboring nodes, the allocation of the total amount of interactions to them can be vastly diverse. Even if such potentially heterogeneous interactions epitomized by weighted networks are observable, as a result of the aforementioned ego-centric allocation of interactions, the relative importance or dependency between two interacting nodes can only be implicitly accessible. In this work, we precisely pinpoint such relative dependency by proposing the framework to discover hidden dependent relations extracted from weighted networks. For a given weighted network, we provide a systematic criterion to select the most essential interactions for individual nodes based on the concept of information entropy. The criterion is symbolized by assigning the effective number of neighbors or the effective out-degree to each node, and the resultant directed subnetwork decodes the hidden dependent relations by leaving only the most essential directed interactions. We apply our methodology to two time-stamped empirical network data, namely the international trade relations between nations in the world trade web (WTW) and the network of people in the historical record of Korea, Annals of the Joseon Dynasty (AJD). Based on the data analysis, we discover that the properties of mutual dependency encoded in the two systems are vastly different.},
  archivePrefix = {arXiv},
  eprint = {2008.11047},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/KCY9KXCN/Lee et al_2020_Uncovering Hidden Dependency in Weighted Networks via Information Entropy.pdf;/home/aleksandar/Zotero/storage/QFMH82VR/2008.html},
  journal = {arXiv:2008.11047 [physics]},
  primaryClass = {physics}
}

@article{leGovernanceQualitygrowthNexus2020,
  title = {The Governance Quality-Growth Nexus Revisited: {{A}} New Evidence from the {{Bayesian}} Multilevel Generalized Linear Model},
  author = {Le, Anh Hoang and Hoang, Khai Nguyen and Pham, An},
  year = {2020},
  pages = {8},
  file = {/home/aleksandar/Dropbox/Zotero/Le et al/Le et al_2020_The governance quality-growth nexus revisited.pdf},
  language = {en}
}

@article{legramantiBayesianTestingExogenous2020,
  title = {Bayesian {{Testing}} for {{Exogenous Partition Structures}} in {{Stochastic Block Models}}},
  author = {Legramanti, Sirio and Rigon, Tommaso and Durante, Daniele},
  year = {2020},
  month = sep,
  abstract = {Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional information on exogenous node partitions, these sources of knowledge are typically included in the model to supervise the cluster assignment mechanism or to improve inference on edge probabilities. Although these solutions are routinely implemented, there is a lack of formal approaches to test if a given external node partition is in line with the endogenous clustering structure encoding stochastic equivalence patterns among the nodes in the network. To fill this gap, we develop a formal Bayesian testing procedure which relies on the calculation of the Bayes factor between a stochastic block model with known grouping structure defined by the exogenous node partition and an infinite relational model that allows the endogenous clustering configurations to be unknown, random and fully revealed by the block-connectivity patterns in the network. A simple Markov chain Monte Carlo method for computing the Bayes factor and quantifying uncertainty in the endogenous groups is proposed. This routine is evaluated in simulations and in an application to study exogenous equivalence structures in brain networks of Alzheimer's patients.},
  archivePrefix = {arXiv},
  eprint = {2009.12267},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Legramanti et al/Legramanti et al_2020_Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models.pdf;/home/aleksandar/Zotero/storage/W3TVULTC/2009.html},
  journal = {arXiv:2009.12267 [stat]},
  primaryClass = {stat}
}

@article{legramantiExtendedStochasticBlock2020,
  title = {Extended {{Stochastic Block Models}}},
  author = {Legramanti, Sirio and Rigon, Tommaso and Durante, Daniele and Dunson, David B.},
  year = {2020},
  month = jul,
  abstract = {Stochastic block models (SBM) are widely used in network science due to their interpretable structure that allows inference on groups of nodes having common connectivity patterns. Although providing a well established model\textendash based approach for community detection, such formulations are still the object of intense research to address the key problem of inferring the unknown number of communities. This has motivated the development of several probabilistic mechanisms to characterize the node partition process, covering solutions with fixed, random and infinite number of communities. In this article we provide a unified view of all these formulations within a single extended stochastic block model (ESBM), that relies on Gibbs\textendash type processes and encompasses most existing representations as special cases. Connections with Bayesian nonparametric literature open up new avenues that allow the natural inclusion of several unexplored options to model the nodes partition process and to incorporate node attributes in a principled manner. Among these new alternatives, we focus on the Gnedin process as an example of a probabilistic mechanism with desirable theoretical properties and nice empirical performance. A collapsed Gibbs sampler that can be applied to the whole ESBM class is proposed, and refined methods for estimation, uncertainty quantification and model assessment are outlined. The performance of ESBM is assessed in simulations and an application to bill co\textendash sponsorship networks in the Italian parliament, where we find key hidden block structures and core\textendash periphery patterns.},
  archivePrefix = {arXiv},
  eprint = {2007.08569},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/LIX36PIX/Legramanti et al. - 2020 - Extended Stochastic Block Models.pdf},
  journal = {arXiv:2007.08569 [stat]},
  language = {en},
  primaryClass = {stat}
}

@techreport{leisingWhyItemsCorrelate2020,
  title = {Why Do Items Correlate with One Another? {{A}} Conceptual Analysis with Relevance for General Factors and Network Models},
  shorttitle = {Why Do Items Correlate with One Another?},
  author = {Leising, Daniel and Burger, Julian and Zimmermann, Johannes and B{\"a}ckstr{\"o}m, Martin and Oltmanns, Joshua R. and Connelly, Brian S.},
  year = {2020},
  month = aug,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/7c895},
  abstract = {Most psychometric research relies heavily on patterns of correlations between items on which perceivers describe targets. Apart from the targets' actual (``substantive'') characteristics, this type of data has been shown to also reflect a number of other, non-substantive sources of variation. This is problematic because each of these (semantic redundancy, attitudes, formal response styles) may all by itself account for correlations among items, which may then be misinterpreted in terms of substantive effects. We present an integrative theoretical account of how these non-substantive influences may affect the pattern of relationships between items. We also point out how this is relevant to the validity of conclusions drawn in research on ``general factors'' (of personality, psychopathology, and personality pathology) and on ``network models''. Furthermore, we discuss various ways of dealing with the problem, which is necessary before any correlations between items may be interpreted in terms of substantive associations between the targets' actual characteristics.},
  file = {/home/aleksandar/Zotero/storage/9Y9BMGDY/Leising et al_2020_Why do items correlate with one another.pdf},
  type = {Preprint}
}

@article{leLinearRegressionIts2020,
  title = {Linear Regression and Its Inference on Noisy Network-Linked Data},
  author = {Le, Can M. and Li, Tianxi},
  year = {2020},
  month = jul,
  abstract = {Linear regression on a set of observations linked by a network has been an essential tool in modeling the relationship between response and covariates with additional network data. Despite its wide range of applications in many areas, such as social sciences and health-related research, the problem has not been well-studied in statistics so far. Previous methods either lack inference tools or rely on restrictive assumptions on social effects, and usually assume that networks are observed without errors, which is too good to be true in many problems. In this paper, we propose a linear regression model with nonparametric network effects. Our model does not assume that the relational data or network structure is exactly observed; thus, the method can be provably robust to a certain level of perturbation of the network structure. We establish a set of asymptotic inference results under a general requirement of the network perturbation and then study the robustness of our method in the specific setting when the perturbation comes from random network models. We discover a phase-transition phenomenon of inference validity concerning the network density when no prior knowledge about the network model is available, while also show the significant improvement achieved by knowing the network model. A by-product of our analysis is a rate-optimal concentration bound about subspace projection that may be of independent interest. We conduct extensive simulation studies to verify our theoretical observations, and demonstrate the advantage of our method over a few benchmarks in terms of accuracy and computational efficiency under different data-generating models. The method is then applied to adolescent network data to study gender and racial difference in social activities.},
  archivePrefix = {arXiv},
  eprint = {2007.00803},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/JDUQ66M2/Le and Li - 2020 - Linear regression and its inference on noisy netwo.pdf;/home/aleksandar/Zotero/storage/GKJ8J2VT/2007.html},
  journal = {arXiv:2007.00803 [stat]},
  primaryClass = {stat}
}

@article{leongPerceivingOnlinePublic2020,
  title = {Perceiving Online Public Opinion: {{The}} Impact of {{Facebook}} Opinion Cues, Opinion Climate Congruency, and Source Credibility on Speaking Out},
  shorttitle = {Perceiving Online Public Opinion},
  author = {Leong, Alisius D and Ho, Shirley S},
  year = {2020},
  month = jun,
  pages = {1461444820931054},
  publisher = {{SAGE Publications}},
  issn = {1461-4448},
  doi = {10.1177/1461444820931054},
  abstract = {The novel affordances and unique features on social media have transformed the way people assess public opinion. Drawing on the spiral of silence (SOS) theory, this study examines the roles that user-generated comments (UGCs) and aggregated user representations (AURs), represented by reaction emojis, play in shaping perceptions of the opinion climate. It also investigates how features on the platform trigger perceptions of source credibility to influence willingness to speak out. Results from a 3 (opinion cues: UGCs-only vs AURs-only vs UGCs and AURs) \texttimes{} 2 (opinion climate congruency: congruent vs incongruent) \texttimes{} 2 (source credibility: high vs low) between-subjects experimental design revealed that audiences perceived reaction emojis to reflect public opinion. Source credibility was found to affect willingness to speak out and moderate opinion climate congruency. The findings extend the SOS theory by highlighting the significant role that novel affordances have on SOS components operating online. Implications of the findings were discussed.},
  journal = {New Media \& Society},
  language = {en}
}

@article{leszczenskyHowDealReverse,
  ids = {leszczenskyHowDealReversea},
  title = {How to {{Deal With Reverse Causality Using Panel Data}}? {{Recommendations}} for {{Researchers Based}} on a {{Simulation Study}}},
  author = {Leszczensky, Lars and Wolbring, Tobias},
  doi = {10.1177/0049124119882473},
  abstract = {Does \emph{X} affect \emph{Y}? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel models. Whereas the methodological literature has suggested various alternative solutions, these approaches face many criticisms, chief among them to be sensitive to the correct specification of temporal lags. Applied researchers are thus left with little guidance. Seeking to provide such guidance, we compare how different panel models perform under a range of different conditions. Our Monte Carlo simulations reveal that unlike conventional panel models, a cross-lagged panel model with fixed effects not only offers protection against bias arising from reverse causality under a wide range of conditions but also helps to circumvent the problem of misspecified temporal lags.},
  journal = {Sociological Methods and Research}
}

@article{leveneHumanDynamicsLimited,
  title = {Human Dynamics with Limited Complexity},
  author = {Levene, Mark and Fenner, Trevor and Loizou, George},
  doi = {10.1080/17445760.2019.1578881},
  abstract = {Human dynamics suggest statistical models that may explain and provide us with better insight into human behaviour in various social contexts. Here, we provide a critical overview of human dynamics in the context of complex systems and sociophysics. One of the principal ideas in sociophysics is that, in a similar framework to that of statistical physics, individual humans can be thought of as `social atoms', each exhibiting simple individual behaviour and possessing limited intelligence, but nevertheless collectively giving rise to complex social patterns. In this context, we propose a generative multiplicative decrease process having an \emph{attrition function} that controls the rate of decrease of the population at each stage of the process. The discrete solution to the model takes the form of a product, and a continuous approximation of this solution is derived via the renewal equation that describes age-structured population dynamics. We also investigate some limited complexity variations of the attrition function within this model.},
  journal = {International Journal of Parallel, Emergent and Distributed Systems}
}

@article{levickHindsightBiasExpert2020,
  title = {Hindsight Bias in Expert Surveys: {{How}} Democratic Crises Influence Retrospective Evaluations},
  shorttitle = {Hindsight Bias in Expert Surveys},
  author = {Levick, Laura and {Olavarria-Gambi}, Mauricio},
  year = {2020},
  month = mar,
  pages = {0263395720914571},
  publisher = {{SAGE Publications Ltd}},
  issn = {0263-3957},
  doi = {10.1177/0263395720914571},
  abstract = {Expert surveys provide a standardized way to access and synthesize specialized knowledge, thereby, enabling the analysis of a diverse range of concepts and contexts that might otherwise be difficult to approach systematically. However, while studies of public opinion have long argued that cognitive biases represent potential problems when it comes to the general population, less attention has been paid to similar issues among expert respondents. This study examines one form of cognitive bias, hindsight bias. Hindsight bias refers to the tendency to retrospectively exaggerate one's foresight of a particular event. We argue that hindsight bias is a potential problem when it comes to retrospective evaluation due to the difficulty involved in separating our assessments of the pre-crisis period from the knowledge that a crisis occurred. Using disaggregated data from the Varieties of Democracy Project, we look for evidence of hindsight bias in coders' evaluations of the periods that preceded major crises of democracy. We find that coder disagreement is significantly higher in pre-crisis scenarios than in our control group. Concerningly, despite this disagreement, coders remain similarly confident in their assessments. This represents a potential problem for those who seek to use these data to study democratic breakdowns and transitions.},
  journal = {Politics},
  language = {en}
}

@article{levitskyRiseCompetitiveAuthoritarianism2002,
  title = {The {{Rise}} of {{Competitive Authoritarianism}}},
  author = {Levitsky, Steven and Way, Lucan},
  year = {2002},
  volume = {13},
  pages = {51--65},
  issn = {1086-3214},
  doi = {10.1353/jod.2002.0026},
  file = {/home/aleksandar/Dropbox/Zotero/Levitsky_Way/Levitsky_Way_2002_The Rise of Competitive Authoritarianism.pdf},
  journal = {Journal of Democracy},
  language = {en},
  number = {2}
}

@article{lewis-beckElectionForecastingToo,
  title = {Election {{Forecasting}}: {{Too Far Out}}?},
  author = {{Lewis-Beck}, Michael and Wlezien, Christopher},
  pages = {43},
  abstract = {We consider two criteria for evaluating election forecasts: accuracy (precision) and lead (distance from the event), specifically the trade-off between the two in poll-based forecasts. We evaluate how much ``lead'' still allows prediction of the election outcome. How much further back can we go, supposing we tolerate a little more error? Our analysis offers estimates of the ``optimal'' lead time for election forecasts, based on a dataset of over 26,000 vote intention polls from 338 elections in 44 countries between 1942 and 2014. We find that optimization of a forecast is possible, and typically occurs two to three months before the election, but can be influenced by the arrangement of political institutions. To demonstrate how our optimization guidelines perform in practice, we consider recent elections in the UK, the US and France.},
  language = {en}
}

@article{lewisPartySystemsPostcommunist2006,
  title = {Party Systems in Post-Communist {{Central Europe}}: {{Patterns}} of Stability and Consolidation},
  shorttitle = {Party Systems in Post-Communist {{Central Europe}}},
  author = {Lewis, Paul G.},
  year = {2006},
  month = aug,
  volume = {13},
  pages = {562--583},
  issn = {1351-0347, 1743-890X},
  doi = {10.1080/13510340600791863},
  journal = {Democratization},
  language = {en},
  number = {4}
}

@article{lewisPromiseDynamicSystems2000,
  title = {The {{Promise}} of {{Dynamic Systems Approaches}} for an {{Integrated Account}} of {{Human Development}}},
  author = {Lewis, Marc D.},
  year = {2000},
  volume = {71},
  pages = {36--43},
  issn = {1467-8624},
  doi = {10.1111/1467-8624.00116},
  abstract = {After decades of theoretical fragmentation and insularity, a converging explanatory framework based on general scientific principles is an important goal for developmental psychology. Dynamic systems approaches may provide such a framework, using principles of self-organization to explain how novel forms emerge without predetermination and become increasingly complex with development. New trends in traditional theoretical families emphasize systemic, emergent processes, and these can now be explicated with principles of self-organization that apply to all natural systems. Self-organization thus provides a single explanation for the multiple facets of development, integrating diverse developmental viewpoints within a larger scientific perspective.},
  annotation = {\_eprint: https://srcd.onlinelibrary.wiley.com/doi/pdf/10.1111/1467-8624.00116},
  file = {/home/aleksandar/Dropbox/Zotero/Lewis/Lewis_2000_The Promise of Dynamic Systems Approaches for an Integrated Account of Human.pdf;/home/aleksandar/Zotero/storage/39WH4S3H/1467-8624.html;/home/aleksandar/Zotero/storage/RYQB6JST/1467-8624.html},
  journal = {Child Development},
  language = {en},
  number = {1}
}

@article{liAnalysisCovarianceRandomized2019,
  title = {Analysis of {{Covariance}} in {{Randomized Experiments}} with {{Heterogeneity}} of {{Regression}} and a {{Random Covariate}}: {{The Variance}} of the {{Estimated Treatment Effect}} at {{Selected Covariate Values}}},
  shorttitle = {Analysis of {{Covariance}} in {{Randomized Experiments}} with {{Heterogeneity}} of {{Regression}} and a {{Random Covariate}}},
  author = {Li, Li and McLouth, Christopher J. and Delaney, Harold D.},
  year = {2019},
  month = dec,
  volume = {0},
  pages = {1--15},
  issn = {0027-3171},
  doi = {10.1080/00273171.2019.1693953},
  abstract = {Researchers detecting heterogeneity of regression in a treatment outcome study including a covariate and random assignment to groups often want to investigate the simple treatment effect at the sample grand mean of the covariate and at points one standard deviation above and below that mean. The estimated variances of the simple treatment effect that have traditionally been used in such tests were derived under the assumption that the covariate values were fixed constants. We derive results appropriate for a two-group experiment that instead presume the covariate is a normally distributed random variable. A simulation study is used to confirm the validity of the analytical results and to compare error estimates and confidence intervals based on these results with those based on assuming a fixed covariate. Discrepancies between estimates for fixed and random covariates of the variability of treatment effects can be substantial. However, in situations where the extent of heterogeneity of regression is like that typically reported, presuming the covariate is random rather than fixed will generally result in only a modest increase in estimated standard errors, and in some circumstances can even result in a smaller estimated standard error. We illustrate the new methods with an empirical data set.},
  journal = {Multivariate Behavioral Research},
  number = {0},
  pmid = {31795755}
}

@article{liangEquivalentMeasurePartial2015,
  title = {An Equivalent Measure of Partial Correlation Coefficients for High-Dimensional {{Gaussian}} Graphical Models},
  author = {Liang, Faming and Song, Qifan and Qiu, Peihua},
  year = {2015},
  month = jul,
  volume = {110},
  pages = {1248--1265},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.2015.1012391},
  journal = {Journal of the American Statistical Association},
  language = {en},
  number = {511}
}

@article{liangPriorSensitivityBayesian2020,
  title = {Prior {{Sensitivity}} in {{Bayesian Structural Equation Modeling}} for {{Sparse Factor Loading Structures}}},
  author = {Liang, Xinya},
  year = {2020},
  month = feb,
  pages = {0013164420906449},
  publisher = {{SAGE Publications Inc}},
  issn = {0013-1644},
  doi = {10.1177/0013164420906449},
  abstract = {Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both variable selection and model estimation. The prior sensitivity in BSEM-N was explored in factor analysis models with sparse loading structures through a simulation study (Study 1) and an empirical example (Study 2). Study 1 examined the prior sensitivity in BSEM-N based on the model fit, population model recovery, true and false positive rates, and parameter estimation. Seven shrinkage priors on cross-loadings and five noninformative/vague priors on other model parameters were examined. Study 2 provided a real data example to illustrate the impact of various priors on model fit and parameter selection and estimation. Results indicated that when the 95\% credible intervals of shrinkage priors barely covered the population cross-loading values, it resulted in the best balance between true and false positives. If the goal is to perform variable selection, a sparse cross-loading structure is required, preferably with a minimal number of nontrivial cross-loadings and relatively high primary loading values. To improve parameter estimates, a relatively large prior variance is preferred. When cross-loadings are relatively large, BSEM-N with zero-mean priors is not recommended for the estimation of cross-loadings and factor correlations.},
  journal = {Educational and Psychological Measurement},
  language = {en}
}

@article{liaoIndividualComponentsThree,
  ids = {liaoIndividualComponentsThreea},
  title = {Individual {{Components}} of {{Three Inequality Measures}} for {{Analyzing Shapes}} of {{Inequality}}},
  author = {Liao, Tim Futing},
  doi = {10.1177/0049124119875961},
  abstract = {In common sociological research, income inequality is measured only at the aggregate level. The main purpose of this article is to demonstrate that there is more than meets the eye when inequality is indicated by a single measure. In this article, I introduce an alternative method that evaluates individuals' contributions to inequality as well as the between-group and within-group components of these individual contributions. I first highlight three common inequality measures, the Gini index and two generalized entropy measures\textemdash Theil's T and Theil's L indices\textemdash by presenting their individual components as a method for evaluating inequality. Five artificial data examples illustrate the use of these individual components first. An empirical analysis of the 2007 and 2017 Current Population Survey data then focuses on the differences in inequality revealed by such individual inequality components between the 2007 and 2017. The individual-level inequality measures can reveal patterns of inequality concealed by single measures at the aggregate level. In particular, the Gini individual measures differentiate cases better than the generalized entropy measures and tend to have smaller standard errors in a regression analysis.},
  journal = {Sociological Methods and Research}
}

@article{liAsymptoticDistributionModularity,
  title = {Asymptotic Distribution of Modularity in Networks},
  author = {Li, Yang and Qi, Yongcheng},
  doi = {10.1007/s00184-019-00740-7},
  abstract = {The structure of complex networks is an important aspect in the study of the real network data. Quite often, it is desirable to know the division of the network into communities. A large number of community detection algorithms have been proposed to probe the community structure of complex networks. For a specific partition of a given network, we show that the distribution of modularity under a null hypothesis of free labeling is asymptotically normal when the size of the network gets large. The significance of the partition is defined based on this asymptotic distribution, which can help assess its goodness. Two different partitions can also be compared statistically. Simulation studies and real data analyses are performed for illustration.},
  journal = {Metrika}
}

@article{liAsymptoticDistributionModularitya,
  title = {Asymptotic Distribution of Modularity in Networks},
  author = {Li, Yang and Qi, Yongcheng},
  doi = {10.1007/s00184-019-00740-7},
  abstract = {The structure of complex networks is an important aspect in the study of the real network data. Quite often, it is desirable to know the division of the network into communities. A large number of community detection algorithms have been proposed to probe the community structure of complex networks. For a specific partition of a given network, we show that the distribution of modularity under a null hypothesis of free labeling is asymptotically normal when the size of the network gets large. The significance of the partition is defined based on this asymptotic distribution, which can help assess its goodness. Two different partitions can also be compared statistically. Simulation studies and real data analyses are performed for illustration.},
  journal = {Metrika}
}

@article{liAsymptoticDistributionModularityb,
  title = {Asymptotic Distribution of Modularity in Networks},
  author = {Li, Yang and Qi, Yongcheng},
  doi = {10.1007/s00184-019-00740-7},
  abstract = {The structure of complex networks is an important aspect in the study of the real network data. Quite often, it is desirable to know the division of the network into communities. A large number of community detection algorithms have been proposed to probe the community structure of complex networks. For a specific partition of a given network, we show that the distribution of modularity under a null hypothesis of free labeling is asymptotically normal when the size of the network gets large. The significance of the partition is defined based on this asymptotic distribution, which can help assess its goodness. Two different partitions can also be compared statistically. Simulation studies and real data analyses are performed for illustration.},
  journal = {Metrika}
}

@article{liBayesianModelSelection2020,
  title = {Bayesian Model Selection Approach for Colored Graphical {{Gaussian}} Models},
  author = {Li, Qiong and Gao, Xin and Massam, Helene},
  year = {2020},
  month = apr,
  abstract = {We consider a class of colored graphical Gaussian models obtained by placing symmetry constraints on the precision matrix in a Bayesian framework. The prior distribution on the precision matrix is the colored G-Wishart prior which is the Diaconis-Ylvisaker conjugate prior. In this paper, we develop a computationally efficient model search algorithm which combines linear regression with a double reversible jump Markov chain Monte Carlo (MCMC) method. The latter is to estimate the Bayes factors expressed as the ratio of posterior probabilities of two competing models. We also establish the asymptotic consistency property of the model selection procedure based on the Bayes factors. Our procedure avoids an exhaustive search which is computationally impossible. Our method is illustrated with simulations and a real-world application with a protein signalling data set.},
  archivePrefix = {arXiv},
  eprint = {2004.00764},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Li et al/Li et al_2020_Bayesian model selection approach for colored graphical Gaussian models.pdf},
  journal = {arXiv:2004.00764 [stat]},
  language = {en},
  primaryClass = {stat}
}

@article{liBoundedConfidenceDynamics,
  title = {Bounded Confidence Dynamics and Graph Control: Enforcing Consensus},
  author = {Li, GuanLin and Motsch, Sebastien and Weber, Dylan},
  pages = {26},
  abstract = {A generic feature of bounded confidence type models is the formation of clusters of agents. We propose and study a variant of bounded confidence dynamics with the goal of inducing unconditional convergence to a consensus. The defining feature of these dynamics which we name the No one left behind dynamics is the introduction of a local control on the agents which preserves the connectivity of the interaction network. We rigorously demonstrate that these dynamics result in unconditional convergence to a consensus. The qualitative nature of our argument prevents us quantifying how fast a consensus emerges, however we present numerical evidence that sharp convergence rates would be challenging to obtain for such dynamics. Finally, we propose a relaxed version of the control. The dynamics that result maintain many of the qualitative features of the bounded confidence dynamics yet ultimately still converges to a consensus as the control still maintains connectivity of the interaction network.},
  file = {/home/aleksandar/Dropbox/Zotero/Li et al/Li et al_Bounded conﬁdence dynamics and graph control.pdf},
  language = {en}
}

@article{lichteblauPartiesPerceiveTheir2020,
  title = {Do Parties Perceive Their Voter Potentials Correctly? {{Reconsidering}} the Spatial Logic of Electoral Competition},
  shorttitle = {Do Parties Perceive Their Voter Potentials Correctly?},
  author = {Lichteblau, Josephine and Giebler, Heiko and Wagner, Aiko},
  year = {2020},
  month = jun,
  volume = {65},
  pages = {102126},
  issn = {0261-3794},
  doi = {10.1016/j.electstud.2020.102126},
  abstract = {Political parties strive for maximizing their vote shares. One way to achieve this goal is to attract voters from competitors. A precondition for strategies aiming at attracting these voters is that parties perceive their voter potentials among their rivals' electorates correctly. Yet, hardly anything is known about such perceptions. To fill this gap, we develop analogue measures of a party's perceived and its actual voter potential for each competitor in a party system. Combining elite and mass surveys conducted in Germany, we show that perceived and actual voter potentials depend on spatial considerations but also that not all parties are able to correctly evaluate their potentials. These deviations can be traced back to differences in the perceived placement of political actors between elites and citizens. This supports the spatial logic of party competition but it also points to potential pitfalls for strategic behavior of political parties.},
  journal = {Electoral Studies},
  language = {en}
}

@article{liebovitchChapterIntroductionFractals,
  title = {Chapter 5. {{Introduction}} to {{Fractals}}.},
  author = {Liebovitch, Larry and Shehadeh, Lina},
  file = {/home/aleksandar/Zotero/storage/YD8PTVKY/chap5.pdf}
}

@article{liEffectsCrossloadingsDetermining2020,
  title = {Effects of Cross-Loadings on Determining the Number of Factors to Retain},
  author = {Li, Yujun and Wen, Zhonglin and Hau, Kit-Tai and Yuan, Ke-Hai and Peng, Yafeng},
  year = {2020},
  month = may,
  volume = {0},
  pages = {1--23},
  publisher = {{Routledge}},
  issn = {1070-5511},
  doi = {10.1080/10705511.2020.1745075},
  abstract = {In exploratory factor analysis (EFA), cross-loadings frequently occur in empirical research, but its effects on determining the number of factors to retain are seldom known. In this paper, we analyzed whether and how cross-loadings affected the performance of the parallel analysis (PA), the empirical Kaiser criterion (EKC), the likelihood ratio test (LRT), the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA) in determining the number of factors to retain. A large-scale simulation study was also conducted. A few conclusions can be drawn: (1) overall, PA provides the most accurate performance, especially when data are non-normally distributed; (2) cross-loadings noticeably affect the performance of PA, CFI, and TLI with different patterns, and they virtually have no effect on EKC, LRT, and RMSEA; (3) no method is immune to the sizable detrimental effect of normality assumption violation. Several recommendations were provided.},
  annotation = {\_eprint: https://doi.org/10.1080/10705511.2020.1745075},
  journal = {Structural Equation Modeling: A Multidisciplinary Journal},
  number = {0}
}

@article{lindellSomethingOldSomething2020,
  title = {Something `{{Old}}', {{Something}} `{{New}}'? {{The UK Space}} of {{Political Attitudes After}} the {{Brexit Referendum}}},
  shorttitle = {Something `{{Old}}', {{Something}} `{{New}}'?},
  author = {Lindell, Johan and Ibrahim, Joseph},
  year = {2020},
  month = oct,
  pages = {136078042096598},
  issn = {1360-7804, 1360-7804},
  doi = {10.1177/1360780420965982},
  abstract = {This study focuses on the political attitudes of UK citizens in the aftermath of the `Brexit' vote. It has been argued that differences within electorates across Europe are found in disputes over taxes, redistribution of wealth and social welfare, as much as in divergent ideas on how to deal with globalisation, migration, and climate change. This article uses the 2016\textendash 2017 round of the European Social Survey (N\,=\,1959) to shed light on two important issues in regard to the relationship between `old' and `new' politics. By using multiple correspondence analysis, we first consider the structure, or dimensionality, of the space of political attitudes in contemporary UK society. Contrary to a prevailing discourse that forwards the argument that postmaterial values constitute an altogether separate political dimension in late modernity, we observe that such values collapse into traditional left/right standpoints. Second, we discuss the connection between class (economic capital, cultural capital, and occupational class) and position-takings in the space of political attitudes. We show that class retains a limited effect on political position-takings, where educational capital plays the most important role. The divisions between the politically interested\textendash uninterested, old\textendash young, men\textendash women, and rural\textendash urban are more clearly demarcated than differences between people of different social class positions. Furthermore, polarisation is most prevalent between a highly opinionated, relatively resourceful, small minority of the population.},
  file = {/home/aleksandar/Zotero/storage/2ZUT52TF/Lindell and Ibrahim - 2020 - Something ‘Old’, Something ‘New’ The UK Space of .pdf},
  journal = {Sociological Research Online},
  language = {en}
}

@article{lindstamSignallingIssueSalience,
  title = {Signalling Issue Salience: {{Explaining}} Niche Party Support in Second-Order Elections},
  author = {Lindstam, Emmy},
  doi = {10.1016/j.electstud.2019.03.002},
  abstract = {{$<$}p{$>$}Why do many mainstream party voters switch to voting for niche parties in second-order elections? I develop a simple framework to explain niche party switching in second-order elections and propose that some voters defect strategically to niche parties as a way to signal the salience of an otherwise overlooked issue to their preferred mainstream party. Using panel data from the United Kingdom and Germany, I find that vote switching in second-order elections is more common among those who perceive a mismatch between the party they feel close to and the party perceived as best able to handle an issue of importance to them, as well as when they believe less is at stake and when they place much importance on an overlooked issue.{$<$}/p{$>$}},
  journal = {Electoral Studies}
}

@article{lindstamSignallingIssueSaliencea,
  title = {Signalling Issue Salience: {{Explaining}} Niche Party Support in Second-Order Elections},
  author = {Lindstam, Emmy},
  doi = {10.1016/j.electstud.2019.03.002},
  abstract = {{$<$}p{$>$}Why do many mainstream party voters switch to voting for niche parties in second-order elections? I develop a simple framework to explain niche party switching in second-order elections and propose that some voters defect strategically to niche parties as a way to signal the salience of an otherwise overlooked issue to their preferred mainstream party. Using panel data from the United Kingdom and Germany, I find that vote switching in second-order elections is more common among those who perceive a mismatch between the party they feel close to and the party perceived as best able to handle an issue of importance to them, as well as when they believe less is at stake and when they place much importance on an overlooked issue.{$<$}/p{$>$}},
  journal = {Electoral Studies}
}

@article{lineiraInferringPartyPositions2020,
  title = {Inferring Party Positions across Issue Dimensions},
  author = {Li{\~n}eira, Robert and Mu{\~n}oz, Jordi and Rico, Guillem},
  year = {2020},
  month = apr,
  pages = {1354068820912653},
  publisher = {{SAGE Publications Ltd}},
  issn = {1354-0688},
  doi = {10.1177/1354068820912653},
  abstract = {In this article, we argue that voters use party positions on an issue dimension as a heuristic device to locate parties on other dimensions on which they have less information about. We confirm our argument by fielding two survey experiments in a context with two distinct issue dimensions: the left-right and national dimensions in Catalonia. We find that the position of a hypothetical (but plausible) candidate on a national dimension exerts a great deal of influence on where respondents locate him on the left-right dimension, as well as on a more narrowly-defined socioeconomic dimension, but that the left-right position does not affect the perceived placement on the national divide. This asymmetry can be partly explained by the different nature of these issue domains, and specifically by the distinct ability of the national dimension to activate social group cues that give rise to projection biases. These findings have important implications for our understanding of issue misperceptions and of the strategic choices available to parties in multidimensional policy spaces. The nature of the issue dimension could facilitate why some issue dimensions tend to function as cue givers and others as cue receivers. We discuss the implications of our findings in constraining the strategies available to parties in multidimensional contexts.},
  journal = {Party Politics},
  language = {en}
}

@article{liNovelCommunityDetection2020,
  title = {A {{Novel Community Detection Algorithm}} Based on {{Paring}}, {{Splitting}} and {{Aggregating}} in {{Internet}} of {{Things}}},
  author = {Li, Yi-zhe and Xia, Hui and Zhang, Rui and Xu, Hu-ben and Cheng, Xiang-Guo},
  year = {2020},
  pages = {1--1},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2020.3006029},
  abstract = {The explosive growth of Internet of Things (i.e., IoT) terminal equipment makes its topology more complex, which leads to the increasing cost of network research. Recently, the implicit community structure is widely used to improve the efficiency of research. However, most of the non-overlapping community detection algorithms have some weakness, such as the large number of community detected and the obvious scale gap between communities. To address these abovementioned problems, we design a novel non-overlapping community detection algorithm, named as Pairing, Splitting and Aggregating algorithm (i.e., PSA). Firstly, in order to improve the accuracy of community division, a new node similarity index is designed to transform the network into a large number of similar node pairs. Secondly, based on the connected branches composed of similar node pairs, the network is further divided into several similar node sets. Thirdly, to balance the scale gap of different communities, the Grasshopper Optimization Algorithm, (i.e., GOA) is introduced to combine the local attribute (i.e., conductance) and global attribute (i.e., modularity) together to aggregate similar node sets into potential (or final) communities. Finally, the experimental results show that PSA not only controls the difference among communities well, but also outperforms the other four popular algorithms in terms of two metrics. Moreover, we propose a community-based resource discovery method (or scheme), named as Community-assisted Short-distance-query Resource Discovery algorithm (i.e., CSRD) to further verify the efficiency of PSA. The results show that the resource discovery efficiency of CSRD using PSA is better compared with other algorithms.},
  file = {/home/aleksandar/Dropbox/Zotero/Li et al/Li et al_2020_A Novel Community Detection Algorithm based on Paring, Splitting and.pdf;/home/aleksandar/Zotero/storage/5WQQK7ZS/9130144.html},
  journal = {IEEE Access},
  keywords = {TODO}
}

@article{linRankReductionHighdimensional,
  title = {Rank Reduction for High-Dimensional Generalized Additive Models},
  author = {Lin, Hongmei and Lian, Heng and Liang, Hua},
  doi = {10.1016/j.jmva.2019.05.005},
  abstract = {{$<$}p{$>$}When a regression problem contains multiple predictors, additive models avoid the difficulty of fitting multivariate functions and at the same time retain some nonlinearity of the model. When the dimension is high, the necessity to estimate a large number of functions, even though univariate, can cause concerns regarding statistical efficiency. We propose a rank reduction approach that assumes that all functions share a small common set of latent functions, which allows borrowing information from a large number of functions. The idea is general and could be used in any model with a large number of functions to estimate, but here we restrict our attention to generalized additive models, especially logistic models, that can deal with discrete responses and is useful for classification. Numerical results are reported to illustrate the finite sample performance of the estimator. We also establish an improved convergence rate of the rank reduction approach compared to the standard estimator and extend it to sparse modeling to deal with an even larger number of predictors.{$<$}/p{$>$}},
  journal = {Journal of Multivariate Analysis}
}

@article{linRankReductionHighdimensionala,
  title = {Rank Reduction for High-Dimensional Generalized Additive Models},
  author = {Lin, Hongmei and Lian, Heng and Liang, Hua},
  doi = {10.1016/j.jmva.2019.05.005},
  abstract = {{$<$}p{$>$}When a regression problem contains multiple predictors, additive models avoid the difficulty of fitting multivariate functions and at the same time retain some nonlinearity of the model. When the dimension is high, the necessity to estimate a large number of functions, even though univariate, can cause concerns regarding statistical efficiency. We propose a rank reduction approach that assumes that all functions share a small common set of latent functions, which allows borrowing information from a large number of functions. The idea is general and could be used in any model with a large number of functions to estimate, but here we restrict our attention to generalized additive models, especially logistic models, that can deal with discrete responses and is useful for classification. Numerical results are reported to illustrate the finite sample performance of the estimator. We also establish an improved convergence rate of the rank reduction approach compared to the standard estimator and extend it to sparse modeling to deal with an even larger number of predictors.{$<$}/p{$>$}},
  journal = {Journal of Multivariate Analysis}
}

@article{liOptimizationIdentifiabilityEfficient2020,
  title = {Optimization of Identifiability for Efficient Community Detection},
  author = {Li, Hui-Jia and Wang, Lin and Zhang, Yan and Perc, Matja{\v z}},
  year = {2020},
  month = jun,
  volume = {22},
  pages = {063035},
  issn = {1367-2630},
  doi = {10.1088/1367-2630/ab8e5e},
  abstract = {Many physical and social systems are best described by networks. And the structural properties of these networks often critically determine the properties and function of the resulting mathematical models. An important method to infer the correlations between topology and function is the detection of community structure, which plays a key role in the analysis, design, and optimization of many complex systems. The nonnegative matrix factorization has been used prolifically to that effect in recent years, although it cannot guarantee balanced partitions, and it also does not allow a proactive computation of the number of communities in a network. This indicates that the nonnegative matrix factorization does not satisfy all the nonnegative low-rank approximation conditions. Here we show how to resolve this important open problem by optimizing the identifiability of community structure. We propose a new form of nonnegative matrix decomposition and a probabilistic surrogate learning function that can be solved according to the majorization\textendash minimization principle. Extensive in silico tests on artificial and real-world data demonstrate the efficient performance in community detection, regardless of the size and complexity of the network.},
  file = {/home/aleksandar/Dropbox/Zotero/Li et al/Li et al_2020_Optimization of identifiability for efficient community detection.pdf},
  journal = {New Journal of Physics},
  language = {en},
  number = {6}
}

@book{lippmann1946public,
  title = {Public Opinion},
  author = {Lippmann, W.},
  year = {1946},
  publisher = {{Penguin Books}},
  isbn = {978-1-4128-3240-3},
  lccn = {46005192},
  series = {Pelican Books}
}

@inproceedings{liPredictingUserInfluence2020,
  title = {Predicting {{User Influence}} in the {{Propagation}} of {{Toxic Information}}},
  booktitle = {Knowledge {{Science}}, {{Engineering}} and {{Management}}},
  author = {Li, Shu and Zhang, Yishuo and Jiang, Penghui and Li, Zhao and Zhang, Chengwei and Liu, Qingyun},
  editor = {Li, Gang and Shen, Heng Tao and Yuan, Ye and Wang, Xiaoyang and Liu, Huawen and Zhao, Xiang},
  year = {2020},
  pages = {459--470},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-55130-8_40},
  abstract = {With the advances of information technology, the Internet has become an indispensable part of life. At the same time, toxic Information has become virulent and common on the Internet. Such information propagation can have a negative impact on individuals, organisations and the society. Traditional approaches, such as detecting texts and posts with toxic Information will eventually generate `dark pools in which the online propagation of toxic information will flourish. In this study, we pay attention to influential users who evidently affect others in the activities related to toxic information. A method of predicting user influence was proposed. Compared to the existing literature, user influence is assessed on the basis of users' text-based and behaviors-based characteristics rather than the network structures only. Moreover, whether the influential users have always been those with strong connections on the social networking site is also discussed. The effectiveness of the proposed method is demonstrated in two real-world datasets.},
  isbn = {978-3-030-55130-8},
  language = {en},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@article{liPredictionModelBased2020,
  title = {Prediction {{Model Based}} on {{Integrated Political Economy System}}: {{The Case}} of {{US Presidential Election}}},
  shorttitle = {Prediction {{Model Based}} on {{Integrated Political Economy System}}},
  author = {Li, Lingbo and Fan, Ying and Zeng, An and Di, Zengru},
  year = {2020},
  month = apr,
  abstract = {This paper studies an integrated system of political and economic systems from a systematic perspective to explore the complex interaction between them, and specially analyzes the case of the US presidential election forecasting. Based on the signed association networks of industrial structure constructed by economic data, our framework simulates the diffusion and evolution of opinions during the election through a kinetic model called the Potts Model. Remarkably, we propose a simple and efficient prediction model for the US presidential election, and meanwhile inspire a new way to model the economic structure. Findings also highlight the close relationship between economic structure and political attitude. Furthermore, the case analysis in terms of network and economy demonstrates the specific features and the interaction between political tendency and industrial structure in a particular period, which is consistent with theories in politics and economics.},
  archivePrefix = {arXiv},
  eprint = {2004.13949},
  eprinttype = {arxiv},
  journal = {arXiv:2004.13949 [physics]},
  language = {en},
  primaryClass = {physics}
}

@article{liProbabilityRobustInference,
  title = {The Probability of a Robust Inference for Internal Validity},
  author = {Li, Tenglong and Frank, Ken},
  doi = {10.1177/0049124120914922},
  abstract = {The internal validity of observational study is often subject to debate. In this study, we define the counterfactuals as the unobserved sample and intend to quantify its relationship with the null hypothesis statistical testing (NHST). We propose the probability of a robust inference for internal validity, that is, the PIV, as a robustness index of causal inference. Formally, the PIV is the probability of rejecting the null hypothesis again based on both the observed sample and the counterfactuals, provided the same null hypothesis has already been rejected based on the observed sample. Under either frequentist or Bayesian framework, one can bound the PIV of an inference based on his bounded belief about the counterfactuals, which is often needed when the unconfoundedness assumption is dubious. The PIV is equivalent to statistical power when the NHST is thought to be based on both the observed sample and the counterfactuals. We summarize the process of evaluating internal validity with the PIV into a six-step procedure and illustrate it with an empirical example.},
  journal = {Sociological Methods and Research}
}

@inbook{lipsetCleavageStructuresParty1967,
  title = {Cleavage Structures, Party Systems, and Voter Alignments: {{An}} Introduction},
  booktitle = {Party {{Systems}} and Voter Alignments: {{Cross}}-National Perspectives},
  author = {Lipset, S.M. and Rokkan, S.},
  year = {1967},
  pages = {1--64},
  publisher = {{The Free Press}},
  address = {{New York}},
  collaborator = {Lipset, S.M. and Rokkan, S.}
}

@article{liSocialInfluenceBased2020,
  title = {Social Influence Based Community Detection in Event-Based Social Networks},
  author = {Li, Xiao and Sun, Chenna and Zia, Muhammad Azam},
  year = {2020},
  month = nov,
  volume = {57},
  pages = {102353},
  issn = {0306-4573},
  doi = {10.1016/j.ipm.2020.102353},
  abstract = {In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional social influence computation method in EBSNs. Therefore, to quantify the pairwise social influence accurately in EBSNs, we first propose to compute two types of social influences, i.e., structure-based social influence and behaviour-based social influence, by utilizing the online social network structure and offline social behaviours of users. In particular, based on the specific features of EBSNs, the similarities of user preference on three aspects (i.e., topics, regions and organizers) are utilized to measure the behaviour-based social influence. Then, we obtain the unified pairwise social influence by combining these two types of social influences through a weight function. Next, we present a social influence based community detection algorithm which is referred to as SICD. In SICD, inspired by the nonlinear feature learning ability of the autoencoder, we first devise a neighborhood based deep autoencoder algorithm to obtain nonlinear community-oriented latent representations of users, and then utilize the k-means algorithm for community detection. Experimental results conducted on real-world dataset show the effectiveness of our proposed algorithm.},
  file = {/home/aleksandar/Zotero/storage/4KUVMAET/S0306457320308487.html},
  journal = {Information Processing \& Management},
  language = {en},
  number = {6}
}

@article{litvakUncoveringDisassortativityLarge2013,
  title = {Uncovering Disassortativity in Large Scale-Free Networks},
  author = {Litvak, Nelly and {van der Hofstad}, Remco},
  year = {2013},
  month = feb,
  volume = {87},
  pages = {022801},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevE.87.022801},
  abstract = {Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, and social and biological networks, are often characterized by degree-degree dependencies between neighboring nodes. In this paper, we propose a new way of measuring degree-degree dependencies. One of the problems with the commonly used assortativity coefficient is that in disassortative networks its magnitude decreases with the network size. We mathematically explain this phenomenon and validate the results on synthetic graphs and real-world network data. As an alternative, we suggest to use rank correlation measures such as Spearman's {$\rho$}. Our experiments convincingly show that Spearman's {$\rho$} produces consistent values in graphs of different sizes but similar structure, and it is able to reveal strong (positive or negative) dependencies in large graphs. In particular, we discover much stronger negative degree-degree dependencies in Web graphs than was previously thought. Rank correlations allow us to compare the assortativity of networks of different sizes, which is impossible with the assortativity coefficient due to its genuine dependence on the network size. We conclude that rank correlations provide a suitable and informative method for uncovering network mixing patterns.},
  file = {/home/aleksandar/Documents/Zotero/Litvak_van der Hofstad/Litvak_van der Hofstad_2013_Uncovering disassortativity in large scale-free networks.pdf;/home/aleksandar/Zotero/storage/AZFBWCDX/PhysRevE.87.html;/home/aleksandar/Zotero/storage/BQGLDG5N/PhysRevE.87.html},
  journal = {Physical Review E},
  keywords = {DONE},
  number = {2}
}

@article{liuBayesianInferenceFinite,
  title = {Bayesian {{Inference}} of {{Finite Population Quantiles}} for {{Skewed Survey Data Using Skew}}-{{Normal Penalized Spline Regression}}},
  author = {Liu, Yutao and Chen, Qixuan},
  doi = {10.1093/jssam/smz016},
  abstract = {Abstract.  Skewed data are common in sample surveys. In probability proportional to size sampling, we propose two Bayesian model-based predictive methods for es},
  file = {/home/aleksandar/Zotero/storage/C9WSQRT5/5559307.html},
  journal = {Journal of Survey Statistics and Methodology},
  language = {en}
}

@article{liuCombinedEffectsOmitting2019,
  title = {The {{Combined Effects}} of {{Omitting Confounders}} and {{Measurement Error}} on {{Statistical Inference}} of {{Mediation}} and a {{New Tool}} for {{Sensitivity Analysis}}},
  author = {Liu, Xiao and Wang, Lijuan},
  year = {2019},
  month = nov,
  volume = {0},
  pages = {1--2},
  issn = {0027-3171},
  doi = {10.1080/00273171.2019.1694478},
  journal = {Multivariate Behavioral Research},
  number = {0},
  pmid = {31773985}
}

@article{liuEffectsStrategyupdatingCost2020,
  title = {Effects of Strategy-Updating Cost on Evolutionary Spatial Prisoner's Dilemma Game},
  author = {Liu, Run-Ran and Jia, Chun-Xiao and Rong, Zhihai},
  year = {2020},
  month = dec,
  volume = {386},
  pages = {125445},
  issn = {0096-3003},
  doi = {10.1016/j.amc.2020.125445},
  abstract = {Strategy-updating rules play fundamental roles for the persistence of cooperation in groups composed by selfish individuals. In this paper, we study the spatial evolutionary prisoner's dilemma game with the introduction of the strategy-updating cost for players, where each player is able to update its strategy if its payoffs is greater than a critical threshold. We show that there exist sudden increases of cooperation level as the temptation to defect increases for a fixed strategy-updating cost, which means a larger temptation to defect cannot always inveigle players into defection, but sometimes promote players to cooperate. This striking phenomenon is in contradiction with the previous wide cognition that a larger temptation to defect always gives rise to a lower cooperation level. This abnormal phenomenon can be explained by a systematic analysis of the payoffs earned by cooperators and defectors and the strategy-transition probabilities between cooperation and defection, respectively. In some cases, the strategy-updating cost can prevent some defectors from becoming cooperators and the increase of temptation to defect enable their payoffs to exceed the threshold of strategy-updating cost. Our results prove that the temptation to defect may have facilitation to the emergence of cooperation in the existence of strategy-updating cost, and thus provide a new understanding of the previously hidden roles of the temptation to defect for the social cooperation.},
  file = {/home/aleksandar/Dropbox/Zotero/Liu et al/Liu et al_2020_Effects of strategy-updating cost on evolutionary spatial prisoner’s dilemma.pdf;/home/aleksandar/Zotero/storage/9PST3KRI/S0096300320304069.html},
  journal = {Applied Mathematics and Computation},
  language = {en}
}

@article{liuEvaluationCommunityDetection2019,
  title = {Evaluation of {{Community Detection Methods}}},
  author = {Liu, Xin and Cheng, Hui-Min and Zhang, Zhong-Yuan},
  year = {2019},
  pages = {1--1},
  issn = {1041-4347, 1558-2191, 2326-3865},
  doi = {10.1109/TKDE.2019.2911943},
  journal = {IEEE Transactions on Knowledge and Data Engineering}
}

@article{liuInterplayHomophilyBasedAppraisal2020,
  title = {Interplay {{Between Homophily}}-{{Based Appraisal Dynamics}} and {{Influence}}-{{Based Opinion Dynamics}}: {{Modeling}} and {{Analysis}}},
  shorttitle = {Interplay {{Between Homophily}}-{{Based Appraisal Dynamics}} and {{Influence}}-{{Based Opinion Dynamics}}},
  author = {Liu, Fangzhou and Cui, Shaoxuan and Mei, Wenjun and Dorfler, Florian and Buss, Martin},
  year = {2020},
  month = may,
  abstract = {In social systems, the evolution of interpersonal appraisals and individual opinions are not independent processes but intertwine with each other. Despite extensive studies on both opinion dynamics and appraisal dynamics separately, no previous work has ever combined these two processes together. In this paper, we propose a novel and intuitive model on the interplay between homophily-based appraisal dynamics and influence-based opinion dynamics. We assume that individuals' opinions are updated via the influence network constructed from their interpersonal appraisals, which are in turn updated based on the individual opinions via the homophily mechanism. By theoretical analysis, we characterize the set of equilibria and some transient behavior of our model. Moreover, we establish the equivalence among the convergence of the appraisal network to social balance, the modulus consensus of individual opinions, and the non-vanishing appraisals. Monte Carlo validations further show that the non-vanishing appraisals condition holds for generic initial conditions. Compared with previous works that explain the emergence of social balance via person-to-person homophily mechanism, our model provides an alternative explanation in terms of the person-to-entity homophily mechanism. In addition, our model also describes how individuals' opinions on multiple irrelevant issues become correlated and converge to modulus consensus over time-varying influence networks.},
  archivePrefix = {arXiv},
  eprint = {2005.11252},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Liu et al/Liu et al_2020_Interplay Between Homophily-Based Appraisal Dynamics and Influence-Based.pdf},
  journal = {arXiv:2005.11252 [cs, eess, math]},
  primaryClass = {cs, eess, math}
}

@article{liuNonequilibriumPhaseTransitions,
  title = {Non-Equilibrium {{Phase Transitions}} in {{2D Small}}-{{World Networks}}: {{Competing Dynamics}}},
  author = {Liu, Wei and Yan, Zhengxin and Zhou, Gaoliang},
  doi = {10.1515/phys-2019-0001},
  abstract = {{$<$}p{$>$}This article offers a detailed analysis of the Ising model in 2D small-world networks with competing Glauber and Kawasaki dynamics. The non-equilibrium stationary state phase transitions are