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

• Published in: IEEE transactions on knowledge and data engineering 2019-11, Vol.31 (11), p.2178-2189
• Main Authors: Ranjbar, Vahid, Salehi, Mostafa, Jandaghi, Pegah, Jalili, Mahdi
• Format: Article
• Language: English
• Subjects: Anomalies; Anomaly detection; Biological system modeling; Clustering; Computer science; Decomposition; Feature extraction; heterogeneous information networks; Image edge detection; Integrated circuit modeling; Mathematical analysis; Methods; Networks; query based anomaly detection; Tensile stress; tensor decomposition; Tensors
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2018.2873391

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.