Graph Filtering to Remove the "Middle Ground" for Anomaly Detection

Discovering patterns and anomalies in a variety of voluminous data represented as a graph is challenging. Current research has demonstrated success discovering graph patterns using a sampling of the data, but there has been little work when it comes to discovering anomalies that are based upon understanding what is normative. In this work we present two approaches to reducing graph data: subgraph filtering and graph filtering. The idea behind the proposed algorithms is the removal of a "murky middle", where data that may not be normative or anomalous, is removed from the discovery process. We empirically validate the proposed approach on real-world, pseudo-real-world, and synthetic data, as well as compare against a similar approach.