GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs

Siddharth Bhatia, Yiwei Wang, Bryan Hooi, Tanmoy Chakraborty

ECML-PKDD 2021 pp. 36-51

doi:10.1007/978-3-030-86520-7_3 /ecmlpkdd/2021/bhatia2021ecmlpkdd-graphanogan/

Abstract

Finding anomalous snapshots from a graph has garnered huge attention recently. Existing studies address the problem using shallow learning mechanisms such as subspace selection, ego-network, or community analysis. These models do not take into account the multifaceted interactions between the structure and attributes in the network. In this paper, we propose GraphAnoGAN, an anomalous snapshot ranking framework, which consists of two core components -- generative and discriminative models. Specifically, the generative model learns to approximate the distribution of anomalous samples from the candidate set of graph snapshots, and the discriminative model detects whether the sampled snapshot is from the ground-truth or not. Experiments on 4 real-world networks show that GraphAnoGAN outperforms 6 baselines with a significant margin (28.29% and 22.01% higher precision and recall, respectively compared to the best baseline, averaged across all datasets).

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Cite

Text

Bhatia et al. "GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021. doi:10.1007/978-3-030-86520-7_3

Markdown

[Bhatia et al. "GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021.](https://mlanthology.org/ecmlpkdd/2021/bhatia2021ecmlpkdd-graphanogan/) doi:10.1007/978-3-030-86520-7_3

BibTeX

@inproceedings{bhatia2021ecmlpkdd-graphanogan,
  title     = {{GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs}},
  author    = {Bhatia, Siddharth and Wang, Yiwei and Hooi, Bryan and Chakraborty, Tanmoy},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2021},
  pages     = {36-51},
  doi       = {10.1007/978-3-030-86520-7_3},
  url       = {https://mlanthology.org/ecmlpkdd/2021/bhatia2021ecmlpkdd-graphanogan/}
}