QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks

TL;DR

This paper introduces a tensor decomposition-based, user-centric anomaly detection method for heterogeneous information networks, outperforming existing techniques in synthetic and real-world tests.

Contribution

It presents a novel tensor decomposition approach combined with clustering for anomaly detection in heterogeneous networks, along with a synthetic network generation model for testing.

Findings

Outperforms state-of-the-art anomaly detection methods

Effective in both synthetic and real-world networks

Provides a user-interactive anomaly detection framework

Abstract

Complex networks have now become integral parts of modern information infrastructures. This paper proposes a user-centric method for detecting anomalies in heterogeneous information networks, in which nodes and/or edges might be from different types. In the proposed anomaly detection method, users interact directly with the system and anomalous entities can be detected through queries. Our approach is based on tensor decomposition and clustering methods. We also propose a network generation model to construct synthetic heterogeneous information network to test the performance of the proposed method. The proposed anomaly detection method is compared with state-of-the-art methods in both synthetic and real-world networks. Experimental results show that the proposed tensor-based method considerably outperforms the existing anomaly detection methods.