Community-Augmented Local-Link Intensity: A Score for Anomaly Detection in Graphs

Graphs can model various systems, activities, or interactions. As a consequence, the ability to detect anomalies in graph-structured data is needed in many scenarios. This paper introduces Community-Augmented Local-Link Intensity (CALLI), an algorithm for quantifying the abnormality extent of nodes in a weighted digraph. Experiments are performed on both synthetically generated and real graphs modeling financial transactions. Throughout the tests, CALLI is directly used as the sole decision-making anomaly score, but also as a feature in a conventional outlier detection approach.