Datasets:

JaySuryavanshi
/

graph-anomaly-questions

license: mit
configs:
  - config_name: nodes
    default: true
    data_files:
      - split: train
        path: nodes.parquet
  - config_name: edges
    data_files:
      - split: train
        path: edges.parquet
task_categories:
  - graph-ml
tags:
  - anomaly-detection
  - graph-anomaly-detection
  - outlier-detection
  - fraud-detection
  - graph
pretty_name: Questions (graph anomaly detection)
size_categories:
  - 10K<n<100K

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}
}