TgraphSpot: Fast and Effective Anomaly Detection for Time-Evolving Graphs
Mirela T. Cazzolato, Saranya Vijayakumar, Xinyi Zheng, Namyong Park, Meng-Chieh Lee, Pedro Fidalgo, Bruno Lages, Agma J. M. Traina, Christos Faloutsos · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Given a large, time-evolving graph of who-calls-whom-when, how can we help analysts find anomalies and fraudsters? How can we explain our decisions? We provide TgraphSpot, which carefully extracts features that are often related to fraud; and which provides informative, interactive plots that help analysts zoom down to the few strange nodes. We present the architecture and design decisions of TgraphSpot. Thanks to our careful feature-extraction algorithms, it scales linearly, taking 2.5 hours on a stock laptop, to process 29 million phone calls. More importantly, when applied on a real dataset of millions of phone calls, it discovered suspicious nodes; experts confirmed that those nodes are fraudsters that had been undetected so far.