Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction
This addresses the challenge of detecting anomalies in complex networks with multiple relation types, with applications ranging from blockchain security to brain disease prediction, though it appears incremental as it builds on existing GNN and GRU methods.
The authors tackled the problem of identifying anomalies in multiplex dynamic networks, proposing ANOMULY, an unsupervised edge anomaly detection framework that achieves state-of-the-art performance on nine real-world datasets.
The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications. However, it raises critical challenges due to the complex nature of anomalies, lack of ground truth knowledge, and complex and dynamic interactions in the network. Most existing approaches usually study networks with a single type of connection between vertices, while in many applications interactions between objects vary, yielding multiplex networks. We propose ANOMULY, a general, unsupervised edge anomaly detection framework for multiplex dynamic networks. In each relation type, ANOMULY sees node embeddings at different GNN layers as hierarchical node states and employs a GRU cell to capture temporal properties of the network and update node embeddings over time. We then add an attention mechanism that incorporates information across different types of relations. Our case study on brain networks shows how this approach could be employed as a new tool to understand abnormal brain activity that might reveal a brain disease or disorder. Extensive experiments on nine real-world datasets demonstrate that ANOMULY achieves state-of-the-art performance.