t_social — T-Social and T-Finance anomaly detection networks

Description

Two anonymized networks of the accounts of an online platform with expert annotations of anomalous accounts, released by Tang, Li, Gao and Li with their graph anomaly detection method BWGNN, and part of the GADBench benchmark:

t_social: a social network, where two accounts are connected if they maintained a friend relationship for more than three months (5,781,065 accounts, 73,105,508 friendships).
t_finance: a transaction network, where two accounts are connected if they have transaction records (39,357 accounts, 21,222,543 edges).

The nodes of both networks have the same kinds of features (10 values related to the registration days, logging activities and interaction frequency of the account) and labels: human experts annotated accounts as anomalous if they fall into categories such as fraud, money laundering and online gambling (3.0% of the accounts in t_social, 4.6% in t_finance). The platform is not named by the authors.

The networks are read from the DGL graph files of GADBench, which store each edge in both directions, and a self-loop for each node; the undirected networks here have one edge per pair, without these self-loops. The random splits of the benchmark (train, validation and test masks) are left out, and so is the edge data count of t_finance, which is 1 for all edges.

The networks have the following properties:

(Node) feature: the 10 features of the account (anonymized, as given).
(Node) label: whether the account is annotated as anomalous (1) or not (0).

Tags

Social Economic Online Unweighted Metadata

Added

2026-10-31

Citation

J. Tang, J. Li, Z. Gao, J. Li, "Rethinking Graph Neural Networks for Anomaly Detection." Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR 162, 21076-21089 (2022) proceedings.mlr.press
J. Tang, F. Hua, Z. Gao, P. Zhao, J. Li, "GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection." Advances in Neural Information Processing Systems 36 (2023) arXiv:2306.12251

Upstream URL OK

https://github.com/squareRoot3/Rethinking-Anomaly-Detection
https://github.com/squareRoot3/GADBench

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
t_social	5,781,065	73,105,508	25.29	51.07	302.75	84254.92	0.12	0.05	45	0.99	Undirected	Unipartite	feature label		145.5 MiB	297.7 MiB	286.3 MiB	316.8 MiB
t_finance	39,357	21,222,543	1078.46	1937.44	5543.94	54.14	0.78	0.89	7	1.00	Undirected	Unipartite	feature label		9.7 MiB	51.3 MiB	49.2 MiB	55.6 MiB