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Araujo M., Papadimitriou S., Gunnemann S., Faloutsos C., Basu P., Swami A., Papalexakis E.E., Koutra D.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

pp 271



Given a large network, changing over time, how can we find patterns and anomalies? We propose Com2, a novel and fast, incremental tensor analysis approach, which can discover both transient and periodic/ repeating communities. The method is (a) scalable, being linear on the input size (b) general, (c) needs no user-defined parameters and (d) effective, returning results that agree with intuition.
We apply our method on real datasets, including a phone-call network and a computer-traffic network. The phone call network consists of 4 million mobile users, with 51 million edges (phonecalls), over 14 days. Com2 spots intuitive patterns, that is, temporal communities (comet communities).
We report our findings, which include large ‘star’-like patterns, nearbipartite- cores, as well as tiny groups (5 users), calling each other hundreds of times within a few days.