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Challenges in Community Discovery on Temporal Networks

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Temporal Network Theory

Part of the book series: Computational Social Sciences ((CSS))

Abstract

Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.

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Notes

  1. 1.

    https://github.com/GiulioRossetti/cdlib/tree/master/cdlib.

  2. 2.

    https://github.com/benmaier/tacoma.

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Author information

Authors and Affiliations

  1. University of Lyon, UCBL/CNRS, Lyon, France

    Remy Cazabet

  2. Knowledge Discovery and Data Mining Lab, ISTI-CNR, Pisa, Italy

    Giulio Rossetti

Authors
  1. Remy Cazabet
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  2. Giulio Rossetti
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Corresponding author

Correspondence to Remy Cazabet .

Editor information

Editors and Affiliations

  1. Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan

    Petter Holme

  2. Department of Computer Science, Aalto University, Espoo, Finland

    Jari Saramäki

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Cazabet, R., Rossetti, G. (2019). Challenges in Community Discovery on Temporal Networks. In: Holme, P., Saramäki, J. (eds) Temporal Network Theory. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-23495-9_10

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