EdgeCentric: Anomaly Detection in Edge-Attributed Networks

TL;DR

EdgeCentric is a scalable anomaly detection method that leverages edge attributes in networks, effectively identifying suspicious behaviors in large real-world graphs like e-commerce and social networks.

Contribution

We introduce EdgeCentric, a novel compression-based approach focusing on edge attributes for anomaly detection, filling a gap in existing graph analysis methods.

Findings

Successfully detects anomalies in large real-world networks

Achieves 0.87 precision on Flipkart e-commerce graph

Demonstrates scalability and effectiveness in diverse datasets

Abstract

Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of number of stars, and edges in online social and phonecall networks contain temporal information about when friendships were formed and when users communicated with each other -- in such cases, edge attributes capture information about how the adjacent nodes interact with other entities in the network. In this paper, we aim to utilize exactly this information to discern suspicious from typical node behavior. 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.