Radar: Residual Analysis for Anomaly Detection in Attributed Networks

Radar: Residual Analysis for Anomaly Detection in Attributed Networks

Jundong Li, Harsh Dani, Xia Hu, Huan Liu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2152-2158. https://doi.org/10.24963/ijcai.2017/299

Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various types of anomalies in real-world attributed networks co-exist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.
Keywords:
Machine Learning: Data Mining
Machine Learning: Unsupervised Learning
Machine Learning: Relational Learning