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

arXiv: https://arxiv.org/abs/1810.08382 · HTML: https://mlsanity.com/paper/1810.08382/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Academic Network Representation via Prediction-Sampling Incorporated Tensor Factorization](https://mlsanity.com/paper/2504.08323/) (2025)

## Abstract (arXiv)

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.