Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models

Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu
Proceedings of the Third Learning on Graphs Conference, PMLR 269:43:1-43:20, 2025.

Abstract

A fundamental challenge confronting supervised graph outlier detection algorithms is the prevalent problem of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance. Recently, generative models, especially diffusion models, have demonstrated their efficacy in synthesizing high-fidelity images. Despite their extraordinary generation quality, their potential in data augmentation for supervised graph outlier detection remains largely underexplored. To bridge this gap, we introduce GODM, a novel data augmentation for mitigating class imbalance in supervised Graph Outlier detection via latent Diffusion Models. Extensive experiments conducted on multiple datasets substantiate the effectiveness and efficiency of GODM. The case study further demonstrated the generation quality of our synthetic data. To foster accessibility and reproducibility, we encapsulate GODM into a plug-and-play package and release it at PyPI: https://pypi.org/project/godm/.

Cite this Paper

BibTeX
@InProceedings{pmlr-v269-liu25b, title = {Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models}, author = {Liu, Kay and Zhang, Hengrui and Hu, Ziqing and Wang, Fangxin and Yu, Philip S.}, booktitle = {Proceedings of the Third Learning on Graphs Conference}, pages = {43:1--43:20}, year = {2025}, editor = {Wolf, Guy and Krishnaswamy, Smita}, volume = {269}, series = {Proceedings of Machine Learning Research}, month = {26--29 Nov}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v269/main/assets/liu25b/liu25b.pdf}, url = {https://proceedings.mlr.press/v269/liu25b.html}, abstract = {A fundamental challenge confronting supervised graph outlier detection algorithms is the prevalent problem of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance. Recently, generative models, especially diffusion models, have demonstrated their efficacy in synthesizing high-fidelity images. Despite their extraordinary generation quality, their potential in data augmentation for supervised graph outlier detection remains largely underexplored. To bridge this gap, we introduce GODM, a novel data augmentation for mitigating class imbalance in supervised Graph Outlier detection via latent Diffusion Models. Extensive experiments conducted on multiple datasets substantiate the effectiveness and efficiency of GODM. The case study further demonstrated the generation quality of our synthetic data. To foster accessibility and reproducibility, we encapsulate GODM into a plug-and-play package and release it at PyPI: https://pypi.org/project/godm/.} }
Endnote
%0 Conference Paper %T Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models %A Kay Liu %A Hengrui Zhang %A Ziqing Hu %A Fangxin Wang %A Philip S. Yu %B Proceedings of the Third Learning on Graphs Conference %C Proceedings of Machine Learning Research %D 2025 %E Guy Wolf %E Smita Krishnaswamy %F pmlr-v269-liu25b %I PMLR %P 43:1--43:20 %U https://proceedings.mlr.press/v269/liu25b.html %V 269 %X A fundamental challenge confronting supervised graph outlier detection algorithms is the prevalent problem of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance. Recently, generative models, especially diffusion models, have demonstrated their efficacy in synthesizing high-fidelity images. Despite their extraordinary generation quality, their potential in data augmentation for supervised graph outlier detection remains largely underexplored. To bridge this gap, we introduce GODM, a novel data augmentation for mitigating class imbalance in supervised Graph Outlier detection via latent Diffusion Models. Extensive experiments conducted on multiple datasets substantiate the effectiveness and efficiency of GODM. The case study further demonstrated the generation quality of our synthetic data. To foster accessibility and reproducibility, we encapsulate GODM into a plug-and-play package and release it at PyPI: https://pypi.org/project/godm/.
APA
Liu, K., Zhang, H., Hu, Z., Wang, F. & Yu, P.S.. (2025). Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models. Proceedings of the Third Learning on Graphs Conference, in Proceedings of Machine Learning Research 269:43:1-43:20 Available from https://proceedings.mlr.press/v269/liu25b.html.

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