  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.
