Community Detection for Temporal Weighted Bipartite Networks

Conference Paper (2023)
Author(s)

Omar F. Fernández Robledo (TU Delft - Multimedia Computing)

M. Klepper (Koninklijke KPN)

E.F.M. Boven (TU Delft - Network Architectures and Services, Koninklijke KPN)

Huijuan Wang (TU Delft - Multimedia Computing)

Multimedia Computing
Copyright
© 2023 O. Fernández Robledo, M. Klepper, E.F.M. van Boven, H. Wang
DOI related publication
https://doi.org/10.1007/978-3-031-21131-7_19
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 O. Fernández Robledo, M. Klepper, E.F.M. van Boven, H. Wang
Multimedia Computing
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
245-257
ISBN (print)
978-3-031-21130-0
ISBN (electronic)
978-3-031-21131-7
Reuse Rights

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Abstract

Community detection of temporal (time-evolving) bipartite networks is challenging because it can be performed either on the temporal bipartite network, or on various projected networks, composed of only one type of nodes, via diverse community detection algorithms. In this paper, we aim to systematically design detection methods addressing both network choices and community detection algorithms, and to compare the community structures detected by different methods. We illustrate our methodology by using a telecommunications network as an example. We find that three methods proposed identify evident community structures: one is performed on each snapshot of the temporal network, and the other two, in temporal projections. We characterise the community structures detected by each method by an evaluation network in which the nodes are the services of the telecommunications network, and the weight of the links between them are the number of snapshots that both services were assigned to the same community. Analysing the evaluation networks of the three methods reveals the similarity and difference among these methods in identifying common node pairs or groups of nodes that often belong to the same community. We find that the two methods that are based on the same projected network identify consistent community structures, whereas the method based on the original temporal bipartite network complements this vision of the community structure. Moreover, we found a non-trivial number of node pairs that belong consistently to the same community in all the methods applied.

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