Self-Discriminative Modeling for Anomalous Graph Detection

Jinyu Cai, Yunhe Zhang, Jicong Fan
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:6387-6408, 2025.

Abstract

Identifying anomalous graphs is essential in real-world scenarios such as molecular and social network analysis, yet anomalous samples are generally scarce and unavailable. This paper proposes a Self-Discriminative Modeling (SDM) framework that trains a deep neural network only on normal graphs to detect anomalous graphs. The neural network simultaneously learns to construct pseudo-anomalous graphs from normal graphs and learns an anomaly detector to recognize these pseudo-anomalous graphs. As a result, these pseudo-anomalous graphs interpolate between normal graphs and real anomalous graphs, which leads to a reliable decision boundary of anomaly detection. In this framework, we develop three algorithms with different computational efficiencies and stabilities for anomalous graph detection. Extensive experiments on 12 different graph benchmarks demonstrated that the three variants of SDM consistently outperform the state-of-the-art GLAD baselines. The success of our methods stems from the integration of the discriminative classifier and the well-posed pseudo-anomalous graphs, which provided new insights for graph-level anomaly detection.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-cai25k, title = {Self-Discriminative Modeling for Anomalous Graph Detection}, author = {Cai, Jinyu and Zhang, Yunhe and Fan, Jicong}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {6387--6408}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/cai25k/cai25k.pdf}, url = {https://proceedings.mlr.press/v267/cai25k.html}, abstract = {Identifying anomalous graphs is essential in real-world scenarios such as molecular and social network analysis, yet anomalous samples are generally scarce and unavailable. This paper proposes a Self-Discriminative Modeling (SDM) framework that trains a deep neural network only on normal graphs to detect anomalous graphs. The neural network simultaneously learns to construct pseudo-anomalous graphs from normal graphs and learns an anomaly detector to recognize these pseudo-anomalous graphs. As a result, these pseudo-anomalous graphs interpolate between normal graphs and real anomalous graphs, which leads to a reliable decision boundary of anomaly detection. In this framework, we develop three algorithms with different computational efficiencies and stabilities for anomalous graph detection. Extensive experiments on 12 different graph benchmarks demonstrated that the three variants of SDM consistently outperform the state-of-the-art GLAD baselines. The success of our methods stems from the integration of the discriminative classifier and the well-posed pseudo-anomalous graphs, which provided new insights for graph-level anomaly detection.} }
Endnote
%0 Conference Paper %T Self-Discriminative Modeling for Anomalous Graph Detection %A Jinyu Cai %A Yunhe Zhang %A Jicong Fan %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-cai25k %I PMLR %P 6387--6408 %U https://proceedings.mlr.press/v267/cai25k.html %V 267 %X Identifying anomalous graphs is essential in real-world scenarios such as molecular and social network analysis, yet anomalous samples are generally scarce and unavailable. This paper proposes a Self-Discriminative Modeling (SDM) framework that trains a deep neural network only on normal graphs to detect anomalous graphs. The neural network simultaneously learns to construct pseudo-anomalous graphs from normal graphs and learns an anomaly detector to recognize these pseudo-anomalous graphs. As a result, these pseudo-anomalous graphs interpolate between normal graphs and real anomalous graphs, which leads to a reliable decision boundary of anomaly detection. In this framework, we develop three algorithms with different computational efficiencies and stabilities for anomalous graph detection. Extensive experiments on 12 different graph benchmarks demonstrated that the three variants of SDM consistently outperform the state-of-the-art GLAD baselines. The success of our methods stems from the integration of the discriminative classifier and the well-posed pseudo-anomalous graphs, which provided new insights for graph-level anomaly detection.
APA
Cai, J., Zhang, Y. & Fan, J.. (2025). Self-Discriminative Modeling for Anomalous Graph Detection. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:6387-6408 Available from https://proceedings.mlr.press/v267/cai25k.html.

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