Datasets:

JaySuryavanshi
/

graph-anomaly-questions

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2	0	-0.003617	-0.05574	-0.00382	0.0239	-0.128337	0.002547	0.038049	0.088025	-0.099858	-0.059896	-0.005775	-0.061997	-0.01826	0.014421	-0.080431	0.006196	0.063556	-0.014254	-0.039845	0.104833	0.017047	-0.038559	0.014902	0.059664	-0.035642	0.030051	0.036455	-0.005677	0.063607	-0.018442	0.008996	0.01183	-0.007595	0.001451	0.007134	-0.04874	-0.02928	-0.112196	0.006951	0.013433	-0.04319	-0.028601	-0.012415	0.001632	-0.01582	-0.056984	0.03757	-0.006981	0.028571	0.149053	0.041809	0.063288	-0.033544	-0.024686	-0.032575	0.057982	-0.041081	-0.032166	0.00163	-0.021081	-0.054294	0.061573	-0.035507	0.045029	-0.11295	0.048066	-0.062003	0.114522	-0.029008	0.022548	0.031477	0.039553	-0.025918	-0.056456	-0.007477	-0.015084	-0.02407	-0.01673	0.021137	0.021456	0.009741	0.007514	0.000301	-0.033315	0.006093	0.009716	0.02108	0.024609	-0.030749	0.013047	-0.065493	-0.019203	0.085926	0.003435	-0.035141	0.003455	-0.000928	-0.021334	-0.039817	0.058023	-0.014413	0.019755	-0.039111	0.028249	0.035074	-0.056294	0.038214	-0.002925	0.04648	-0.006486	-0.074772	0.053494	-0.00487	0.049166	0.014578	0.01699	-0.027375	0.051989	-0.011155	0.040514	0.062885	0.01554	0.027757	0.015979	0.016883	-0.018286	0.03114	-0.010853	0.026117	0.096012	-0.032075	-0.021649	-0.018979	0.038607	-0.007209	0.02478	-0.039948	0.092444	0.019474	0.026958	-0.033926	-0.035146	0.103246	0.038677	-0.050424	0.047247	-0.077396	-0.033841	-0.002568	-0.097975	-0.008677	-0.059823	-0.07653	-0.01762	0.004731	-0.052359	0.021774	0.045493	0.181558	-0.003483	-0.026666	0.018327	-0.047514	-0.055481	-0.108745	0.060553	-0.090748	0.008043	0.024437	0.022856	0.023315	0.040852	0.063426	-0.00707	0.041958	-0.078585	0.082189	-0.064041	-0.06835	0.000126	0.043639	0.026881	-0.076506	-0.062936	-0.066308	-0.004514	-0.023138	-0.021267	0.026766	-0.019022	-0.052772	-0.004508	0.030978	0.106953	-0.038467	0.047322	-0.002552	0.005297	-0.009036	0.061908	0.006363	-0.070915	-0.014085	0.055408	0.003417	-0.030968	0.008228	0.095654	0.004338	-0.009918	0.100985	-0.02273	-0.033842	0.054788	0.024225	0.012959	0.03401	-0.088706	0.02643	0.030657	-0.007341	0.032473	0.009113	0.030562	0.004495	0.025361	0.006234	-0.078559	0.039127	-0.008685	0.06379	0.012913	0.021055	0.010682	0.005921	0.030354	-0.011938	-0.050945	-0.006726	-0.038514	0.015402	0.043904	-0.023971	0.051147	-0.002355	-0.019669	-0.069247	-0.026613	0.030615	-0.059283	0.043316	-0.006765	-0.001764	0.035101	0.082143	0.024303	-0.05948	0.019415	0.017953	-0.116282	-0.047811	0.003003	-0.07727	-0.022648	-0.045258	0.050321	0.020839	0.033347	0.025148	0.01983	0.055221	0.001733	0.099681	-0.026703	0.050771	-0.035378	0.023651	-0.084325	-0.057331	-0.031544	-0.02318	-0.058258	0.045543	0.051347	0.054296	0.051054	0.057436	-0.025385	-0.024999	0.033073	-0.00581	-0.001817	0.00936	-0.008162	-0.011466	-0.078037	0.077316	-0.07188	0.008466	0.000958	false	true	true	false	false	false	true	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	true	true	true	false	false	true	false
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End of preview. Expand in Data Studio

Questions (graph anomaly detection)

Users of the Yandex Q question-answering service, connected by answering interactions. The minority class marks users by activity outcome; at a 3.0% base rate this is a realistic rare-anomaly regime.


Nodes	48,921
Node features	301
Edges	153,540
Outliers	1,460 (3.0%)
Label type	adjudicated
Label source	Yandex Q activity outcome, Platonov et al. 2023

Viewer. Two configs: nodes (default) and edges.

Files. nodes.parquet (node_id, feat_*, label, split masks where available) and edges.parquet (src, dst, the edge list as shipped upstream; symmetrize for undirected use). Ships the upstream 10 split trials as train_mask_0..9, val_mask_0..9, test_mask_0..9.

Load

import pandas as pd

nodes = pd.read_parquet("hf://datasets/JaySuryavanshi/graph-anomaly-questions/nodes.parquet")
edges = pd.read_parquet("hf://datasets/JaySuryavanshi/graph-anomaly-questions/edges.parquet")

As a graph, with graphspot (pip install graphspot):

import numpy as np, scipy.sparse as sp, graphspot
from graphspot.detectors import XGBGraph

n = len(nodes)
adj = sp.csr_matrix((np.ones(len(edges)), (edges.src, edges.dst)), shape=(n, n))
g = graphspot.Graph(adj=adj, x=nodes.filter(like="feat_").to_numpy(),
                    node_labels=nodes.label.to_numpy())

Provenance

Mirrored unmodified (beyond format conversion to parquet) from yandex-research/heterophilous-graphs (MIT). Conversion is scripted and deterministic; label counts above are computed from the files in this repository, not copied from upstream docs.

Citation

Paper: https://arxiv.org/abs/2302.11640

@inproceedings{platonov2023critical,
  title={A critical look at the evaluation of {GNNs} under heterophily: Are we really making progress?},
  author={Platonov, Oleg and Kuznedelev, Denis and Diskin, Michael and Babenko, Artem and Prokhorenkova, Liudmila},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

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Paper for JaySuryavanshi/graph-anomaly-questions

A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Paper • 2302.11640 • Published Feb 22, 2023 • 1