TimeRank: A random walk approach for community discovery in dynamic networks

Ilias Sarantopoulos; Dimitrios Papatheodorou; Dimitrios Vogiatzis; Grigorios Tzortzis; Georgios Paliouras

doi:10.1007/978-3-030-05411-3_28

TimeRank: A random walk approach for community discovery in dynamic networks

Ilias Sarantopoulos^*, Dimitrios Papatheodorou, Dimitrios Vogiatzis, Grigorios Tzortzis, Georgios Paliouras

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

Abstract

In this work we consider the problem of discovering communities in time evolving social networks. We propose TimeRank, an algorithm for dynamic networks, which uses random walks on a tensor representation to detect time-evolving communities. The proposed algorithm is based on an earlier work on community detection in multi-relational networks. Detection of dynamic communities can be be done in two steps (segmentation of the network into time frames, detection of communities per time frame and tracking of communities across time frames). Alternatively it can be done in one step. TimeRank is a one step approach. We compared TimeRank with Non-Negative Tensor Factorisation and Group Evolution Discovery method on synthetic and real world data sets from Reddit.

Original language	English
Title of host publication	Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
Editors	Renaud Lambiotte, Luis M. Rocha, Pietro Lió, Hocine Cherifi, Luca Maria Aiello, Chantal Cherifi
Publisher	SPRINGER
Pages	338-350
Number of pages	13
ISBN (Print)	9783030054106
DOIs	https://doi.org/10.1007/978-3-030-05411-3_28
Publication status	Published - 1 Jan 2019
MoE publication type	A4 Article in a conference publication
Event	International Conference on Complex Networks and their Applications - Cambridge, United Kingdom Duration: 11 Dec 2018 → 13 Dec 2018 Conference number: 7 https://www.complexnetworks.org/

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer
Volume	812
ISSN (Print)	1860-949X

Conference

Conference	International Conference on Complex Networks and their Applications
Abbreviated title	COMPLEX NETWORKS
Country/Territory	United Kingdom
City	Cambridge
Period	11/12/2018 → 13/12/2018
Internet address	https://www.complexnetworks.org/

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Sarantopoulos, I., Papatheodorou, D., Vogiatzis, D., Tzortzis, G., & Paliouras, G. (2019). TimeRank: A random walk approach for community discovery in dynamic networks. In R. Lambiotte, L. M. Rocha, P. Lió, H. Cherifi, L. M. Aiello, & C. Cherifi (Eds.), Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018 (pp. 338-350). (Studies in Computational Intelligence; Vol. 812). SPRINGER. https://doi.org/10.1007/978-3-030-05411-3_28

Sarantopoulos, Ilias ; Papatheodorou, Dimitrios ; Vogiatzis, Dimitrios et al. / TimeRank : A random walk approach for community discovery in dynamic networks. Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. editor / Renaud Lambiotte ; Luis M. Rocha ; Pietro Lió ; Hocine Cherifi ; Luca Maria Aiello ; Chantal Cherifi. SPRINGER, 2019. pp. 338-350 (Studies in Computational Intelligence).

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abstract = "In this work we consider the problem of discovering communities in time evolving social networks. We propose TimeRank, an algorithm for dynamic networks, which uses random walks on a tensor representation to detect time-evolving communities. The proposed algorithm is based on an earlier work on community detection in multi-relational networks. Detection of dynamic communities can be be done in two steps (segmentation of the network into time frames, detection of communities per time frame and tracking of communities across time frames). Alternatively it can be done in one step. TimeRank is a one step approach. We compared TimeRank with Non-Negative Tensor Factorisation and Group Evolution Discovery method on synthetic and real world data sets from Reddit.",

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Sarantopoulos, I, Papatheodorou, D, Vogiatzis, D, Tzortzis, G & Paliouras, G 2019, TimeRank: A random walk approach for community discovery in dynamic networks. in R Lambiotte, LM Rocha, P Lió, H Cherifi, LM Aiello & C Cherifi (eds), Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol. 812, SPRINGER, pp. 338-350, International Conference on Complex Networks and their Applications, Cambridge, United Kingdom, 11/12/2018. https://doi.org/10.1007/978-3-030-05411-3_28

TimeRank : A random walk approach for community discovery in dynamic networks. / Sarantopoulos, Ilias; Papatheodorou, Dimitrios; Vogiatzis, Dimitrios et al.

Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. ed. / Renaud Lambiotte; Luis M. Rocha; Pietro Lió; Hocine Cherifi; Luca Maria Aiello; Chantal Cherifi. SPRINGER, 2019. p. 338-350 (Studies in Computational Intelligence; Vol. 812).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AB - In this work we consider the problem of discovering communities in time evolving social networks. We propose TimeRank, an algorithm for dynamic networks, which uses random walks on a tensor representation to detect time-evolving communities. The proposed algorithm is based on an earlier work on community detection in multi-relational networks. Detection of dynamic communities can be be done in two steps (segmentation of the network into time frames, detection of communities per time frame and tracking of communities across time frames). Alternatively it can be done in one step. TimeRank is a one step approach. We compared TimeRank with Non-Negative Tensor Factorisation and Group Evolution Discovery method on synthetic and real world data sets from Reddit.

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Sarantopoulos I, Papatheodorou D, Vogiatzis D, Tzortzis G, Paliouras G. TimeRank: A random walk approach for community discovery in dynamic networks. In Lambiotte R, Rocha LM, Lió P, Cherifi H, Aiello LM, Cherifi C, editors, Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. SPRINGER. 2019. p. 338-350. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-05411-3_28

TimeRank: A random walk approach for community discovery in dynamic networks

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