Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks
This work addresses anomaly detection in attributed networks, which is important for applications like IoT, finance, and security, but it appears to be incremental in nature.
The paper tackled the problem of uncovering anomalous edges in attributed networks, proposing two complementary methods that improve anomaly identification performance, with experiments on real and synthetic data confirming their effectiveness.
Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and network science applications including {the Internet of Things (IoT)}, finance, security, to list a few. The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths, which can be easily distributed, and hence efficient. The first relies on decomposing the graph data matrix into low rank plus sparse components to markedly improve performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance. The novel methods not only capture anomalous edges linking nodes of different communities, but also spurious connections between any two nodes with different features. Experiments conducted on real and synthetic data corroborate the effectiveness of both methods in the anomaly identification task.