  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.
