EdgeCentric: Anomaly Detection in Edge-Attributed Networks

Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are ubiquitous, and capture rich information about interactions between nodes. In this paper, we aim to utilize exactly this information to discern suspicious from typical behavior in an unsupervised fashion, lending well to the traditional scarcity of ground-truth labels in practical anomaly detection scenarios. Our work has a number of notable contributions, including (a) formulation: while most other graph-based anomaly detection works use structural graph connectivity or node information, we focus on the new problem of leveraging edge information, (b) methodology: we introduce EdgeCentric, an intuitive and scalable compression-based approach for detecting edge-attributed graph anomalies, and (c) practicality: we show that EdgeCentric successfully spots numerous such anomalies in several large, edge-attributed real-world graphs, including the Flipkart e-commerce graph with over 3 million product reviews between 1.1 million users and 545 thousand products, where it achieved 0.87 precision over the top 100 results.