170 research outputs found
Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information
Anomaly detection is defined as discovering patterns that do not conform to
the expected behavior. Previously, anomaly detection was mostly conducted using
traditional shallow learning techniques, but with little improvement. As the
emergence of graph neural networks (GNN), graph anomaly detection has been
greatly developed. However, recent studies have shown that GNN-based methods
encounter challenge, in that no graph anomaly detection algorithm can perform
generalization on most datasets. To bridge the tap, we propose a multi-view
fusion approach for graph anomaly detection (Mul-GAD). The view-level fusion
captures the extent of significance between different views, while the
feature-level fusion makes full use of complementary information. We
theoretically and experimentally elaborate the effectiveness of the fusion
strategies. For a more comprehensive conclusion, we further investigate the
effect of the objective function and the number of fused views on detection
performance. Exploiting these findings, our Mul-GAD is proposed equipped with
fusion strategies and the well-performed objective function. Compared with
other state-of-the-art detection methods, we achieve a better detection
performance and generalization in most scenarios via a series of experiments
conducted on Pubmed, Amazon Computer, Amazon Photo, Weibo and Books. Our code
is available at https://github.com/liuyishoua/Mul-Graph-Fusion.Comment: Graph anomaly detection on attribute networ
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
Despite a surge in interest in GNN development, homogeneity in benchmarking
datasets still presents a fundamental issue to GNN research. GraphWorld is a
recent solution which uses the Stochastic Block Model (SBM) to generate diverse
populations of synthetic graphs for benchmarking any GNN task. Despite its
success, the SBM imposed fundamental limitations on the kinds of graph
structure GraphWorld could create.
In this work we examine how two additional synthetic graph generators can
improve GraphWorld's evaluation; LFR, a well-established model in the graph
clustering literature and CABAM, a recent adaptation of the Barabasi-Albert
model tailored for GNN benchmarking. By integrating these generators, we
significantly expand the coverage of graph space within the GraphWorld
framework while preserving key graph properties observed in real-world
networks. To demonstrate their effectiveness, we generate 300,000 graphs to
benchmark 11 GNN models on a node classification task. We find GNN performance
variations in response to homophily, degree distribution and feature signal.
Based on these findings, we classify models by their sensitivity to the new
generators under these properties. Additionally, we release the extensions made
to GraphWorld on the GitHub repository, offering further evaluation of GNN
performance on new graphs.Comment: Accepted to Workshop on Graph Learning Benchmarks at KDD 202
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