Journal
20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020)
Volume -, Issue -, Pages 966-971Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICDM50108.2020.00110
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Many real-world network data can be modelled as a bipartite graph, such as client-server in the computer network and trader-stock in the stock market. Detecting anomalies on these graphs benefit many applications, e.g., network intrusion detection and illegal trading detection. In real-world networks, these bipartite graphs are not static. Their vertices may change from time to time, and a bipartite graph may demonstrate unexpected burstiness in the form of intermittent increases or decreases in activities between vertices. In this paper, we propose a framework named BEA, a general anomaly detection framework that can effectively detect anomalies on a dynamic bipartite graph with burstiness. We evaluate our proposed framework on three public datasets against the state-of-the-art baselines. We also conduct a case study on the PayPal CSP log network. Through experiments, we show BEA's practicability and effectiveness.
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