2022/01/08 by Ling-Hao Chen, He Li, Chen, Ling-Hao +7
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #FOS: Computer and information sciences #Network Security and Intrusion Detection #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2201.02822
openalex publication_date 2022/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Anomaly detection on attributed networks is widely used in online shopping, financial transactions, communication networks, and so on. However, most existing works trying to detect anomalies on attributed networks only consider a single kind of interaction, so they cannot deal with various kinds of interactions on multi-view attributed networks. It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks. In this paper, we propose a graph convolution-based framework, named AnomMAN, to detect Anomaly on Multi-view Attributed Networks. To jointly consider attributes and all kinds of interactions on multi-view attributed networks, we use the attention mechanism to define the importance of all views in networks. Since the low-pass characteristic of graph convolution operation filters out most high-frequency signals (aonmaly signals), it cannot be directly applied to anomaly detection tasks. AnomMAN introduces the graph auto-encoder module to turn the disadvantage of low-pass features into an advantage. According to experiments on real-world datasets, AnomMAN outperforms the state-of-the-art models and two variants of our proposed model.