Com2: Fast automatic discovery of temporal ('comet') communities

Miguel Araujo; Spiros Papadimitriou; Stephan Günnemann; Christos Faloutsos; Prithwish Basu; Ananthram Swami; Evangelos E. Papalexakis; Danai Koutra

doi:10.1007/978-3-319-06605-9_23

Com2: Fast automatic discovery of temporal ('comet') communities

Miguel Araujo, Spiros Papadimitriou, Stephan Günnemann, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra

Research output: Contribution to journal › Conference article › peer-review

61 Scopus citations

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.

Original language	English
Pages (from-to)	271-283
Number of pages	13
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8444 LNAI
Issue number	PART 2
DOIs	https://doi.org/10.1007/978-3-319-06605-9_23
State	Published - 2014
Externally published	Yes
Event	18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China Duration: 13 May 2014 → 16 May 2014

Keywords

community detection
temporal data
tensor decomposition

Access to Document

10.1007/978-3-319-06605-9_23

Cite this

Araujo, M., Papadimitriou, S., Günnemann, S., Faloutsos, C., Basu, P., Swami, A., Papalexakis, E. E., & Koutra, D. (2014). Com2: Fast automatic discovery of temporal ('comet') communities. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8444 LNAI(PART 2), 271-283. https://doi.org/10.1007/978-3-319-06605-9_23

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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.",

keywords = "community detection, temporal data, tensor decomposition",

author = "Miguel Araujo and Spiros Papadimitriou and Stephan G{\"u}nnemann and Christos Faloutsos and Prithwish Basu and Ananthram Swami and Papalexakis, {Evangelos E.} and Danai Koutra",

year = "2014",

doi = "10.1007/978-3-319-06605-9_23",

language = "English",

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Araujo, M, Papadimitriou, S, Günnemann, S, Faloutsos, C, Basu, P, Swami, A, Papalexakis, EE & Koutra, D 2014, 'Com2: Fast automatic discovery of temporal ('comet') communities', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8444 LNAI, no. PART 2, pp. 271-283. https://doi.org/10.1007/978-3-319-06605-9_23

Com2: Fast automatic discovery of temporal ('comet') communities. / Araujo, Miguel; Papadimitriou, Spiros; Günnemann, Stephan et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8444 LNAI, No. PART 2, 2014, p. 271-283.

Research output: Contribution to journal › Conference article › peer-review

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T1 - Com2

T2 - 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014

AU - Araujo, Miguel

AU - Papadimitriou, Spiros

AU - Günnemann, Stephan

AU - Faloutsos, Christos

AU - Basu, Prithwish

AU - Swami, Ananthram

AU - Papalexakis, Evangelos E.

AU - Koutra, Danai

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - community detection

KW - temporal data

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IS - PART 2

Y2 - 13 May 2014 through 16 May 2014

ER -

Com2: Fast automatic discovery of temporal ('comet') communities

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Keywords

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