Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract)

Authors

  • Hui Tang Renmin University of China
  • Xun Liang Renmin University of China
  • Sensen Zhang Renmin University of China

DOI:

https://doi.org/10.1609/aaai.v38i21.30518

Keywords:

Machine Learning, Graph Anomaly Detection, Graph Representation Learning

Abstract

Detecting anomalies on attributed graphs is a challenging task since labelled anomalies are highly labour-intensive by taking specialized domain knowledge to make anomalous samples not as available as normal ones. Moreover, graphs contain complex structure information as well as attribute information, leading to anomalies that can be typically hidden in the structure space, attribute space, and the mix of both. In this paper, we propose a novel model for graph anomaly detection named ProGAD. Specifically, ProGAD takes advance of label propagation to infer high-quality pseudo labels by considering the structure and attribute inconsistencies between normal and abnormal samples. Meanwhile, ProGAD introduces the prior knowledge of class distribution to correct and refine pseudo labels with a prototype-aware strategy. Experiments demonstrate that ProGAD achieves strong performance compared with the current state-of-the-art methods.

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Published

2024-03-24

How to Cite

Tang, H., Liang, X., & Zhang, S. (2024). Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23667-23668. https://doi.org/10.1609/aaai.v38i21.30518