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 proceedingConference article in proceedingsScientificpeer-review

7 Citations (Scopus)


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 languageEnglish
Title of host publicationComplex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
EditorsRenaud Lambiotte, Luis M. Rocha, Pietro Lió, Hocine Cherifi, Luca Maria Aiello, Chantal Cherifi
Number of pages13
ISBN (Print)9783030054106
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Conference publication
EventInternational Conference on Complex Networks and their Applications - Cambridge, United Kingdom
Duration: 11 Dec 201813 Dec 2018
Conference number: 7

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


ConferenceInternational Conference on Complex Networks and their Applications
Abbreviated titleCOMPLEX NETWORKS
Country/TerritoryUnited Kingdom
Internet address


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