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State-of-the-Art in Community Detection in Temporal Networks

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Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops (AIAI 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 652))

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Abstract

Community detection is a prominent process on networks and has been extensively studied on static networks the last 25 years. This problem concerns the structural partitioning of networks into classes of nodes that are more densely connected when compared to the rest of the network. However, a plethora of real-world networks are highly dynamic, in the sense that entities (nodes) as well as relations between them (edges) constantly change. As a result, many solutions have also been applied in dynamic/temporal networks under various assumptions concerning the modeling of time as well as the emerging communities. The problem becomes quite harder when the notion of time is introduced, since various unseen problems in the static case arise, like the identity problem. In the last few years, a few surveys have been conducted regarding community detection in time-evolving networks. In this survey, our objective is to give a rather condensed but up-to-date overview, when compared to previous surveys, of the current state-of-the-art regarding community detection in temporal networks. We also extend the previous classification of the algorithmic approaches for the problem by discerning between global and local dynamic community detection. The former aims at identifying the evolution of all communities and the latter aims at identifying the evolution of a partition around a set of seed nodes.

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Notes

  1. 1.

    The avalanche effect describes the phenomenon when communities can experience substantial drifts compared to what a static algorithm would find on the static network at a particular time instance.

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Acknowledgment

This research was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers” (Project Number: 3480).

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Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Kostas Christopoulos & Kostas Tsichlas

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  1. Kostas Christopoulos
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  2. Kostas Tsichlas
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Correspondence to Kostas Christopoulos .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Sunderland, Sunderland, UK

    John Macintyre

  4. Universidade do Minho, Guimaraes, Portugal

    Paulo Cortez

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Christopoulos, K., Tsichlas, K. (2022). State-of-the-Art in Community Detection in Temporal Networks. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_30

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  • DOI: https://doi.org/10.1007/978-3-031-08341-9_30

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