Abstract
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.
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Araujo, M. et al. (2014). Com2: Fast Automatic Discovery of Temporal (‘Comet’) Communities. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_23
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DOI: https://doi.org/10.1007/978-3-319-06605-9_23
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