Datasetpublic

JaySuryavanshi/graph-anomaly-questions

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… See the full description on the dataset page: https://huggingface.co/datasets/JaySuryavanshi/graph-anomaly-questions.

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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 (nodeid, `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 trainmask0..9, valmask0..9, testmask0..9`.

Load

python

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):

python

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

bibtex

@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}
}