Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction. (arXiv:2211.08378v1 [cs.LG])

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 …

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