Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912130794520576 |
|---|---|
| author | Liu, Kay Zhang, Hengrui Hu, Ziqing Wang, Fangxin Yu, Philip S. |
| author_facet | Liu, Kay Zhang, Hengrui Hu, Ziqing Wang, Fangxin Yu, Philip S. |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17679 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models Liu, Kay Zhang, Hengrui Hu, Ziqing Wang, Fangxin Yu, Philip S. Machine Learning Social and Information Networks 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/. |
| title | Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2312.17679 |