JaySuryavanshi/graph-anomaly-reddit

Reddit (graph anomaly detection)

Reddit users with post-derived features, with labeled banned users. An organic anomaly dataset; in the BOND benchmark, LOF on raw features beats every deep graph model here, making it a useful honesty check for graph methods.

Nodes	10,984
Node features	64
Edges	168,016
Outliers	366 (3.3%)
Label type	proxy
Label source	banned-user labels via the BOND benchmark

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). No upstream splits; use seeded stratified splits.

Load

import pandas as pd

nodes = pd.read_parquet("hf://datasets/JaySuryavanshi/graph-anomaly-reddit/nodes.parquet")
edges = pd.read_parquet("hf://datasets/JaySuryavanshi/graph-anomaly-reddit/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 pygod-team/data (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/2206.10071

@inproceedings{liu2022bond,
  title={{BOND}: Benchmarking unsupervised outlier node detection on static attributed graphs},
  author={Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Sun, Lichao and Li, Jundong and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}
}