TY - GEN
T1 - Community Detection for Temporal Weighted Bipartite Networks
AU - Robledo, Omar F.
AU - Klepper, Matthijs
AU - Boven, Edgar van
AU - Wang, Huijuan
N1 - 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.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Bipartite networks
KW - Community detection
KW - Temporal networks
UR - http://www.scopus.com/inward/record.url?scp=85149901860&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-21131-7_19
DO - 10.1007/978-3-031-21131-7_19
M3 - Conference contribution
AN - SCOPUS:85149901860
SN - 978-3-031-21130-0
T3 - Studies in Computational Intelligence
SP - 245
EP - 257
BT - Complex Networks and Their Applications XI - Proceedings of The 11th International Conference on Complex Networks and Their Applications
A2 - Cherifi, Hocine
A2 - Mantegna, Rosario Nunzio
A2 - Rocha, Luis M.
A2 - Cherifi, Chantal
A2 - Micciche, Salvatore
PB - Springer
CY - Cham
T2 - 11th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2022
Y2 - 8 November 2022 through 10 November 2022
ER -