BELY, Aliaksandr and Stanislav SOBOLEVSKY. Network Size Reduction Preserving Optimal Modularity and Clique Partition. In Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. Lecture Notes in Computer Science. Cham: Springer, Cham, 2022, p. 19-33. ISBN 978-3-031-10521-0. Available from: https://dx.doi.org/10.1007/978-3-031-10522-7_2.
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Basic information
Original name Network Size Reduction Preserving Optimal Modularity and Clique Partition
Authors BELY, Aliaksandr (112 Belarus, guarantor, belonging to the institution) and Stanislav SOBOLEVSKY (112 Belarus, belonging to the institution).
Edition Cham, Lecture Notes in Computer Science, p. 19-33, 15 pp. 2022.
Publisher Springer, Cham
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10102 Applied mathematics
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14310/22:00128549
Organization unit Faculty of Science
ISBN 978-3-031-10521-0
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-031-10522-7_2
UT WoS 000916469700002
Keywords in English Network size reduction Clustering Community detection Modularity Clique partitioning problem Exact solution
Tags International impact, Reviewed
Changed by Changed by: Mgr. Marie Šípková, DiS., učo 437722. Changed: 1/3/2023 11:52.
Abstract
Graph clustering and community detection are significant and actively developing topics in network science. Uncovering community structure can provide essential information about the underlying system. In this work, we consider two closely related graph clustering problems. One is the clique partitioning problem, and the other is the maximization of partition quality function called modularity. We are interested in the exact solution. However, both problems are NP-hard. Thus the computational complexity of any existing algorithm makes it impossible to solve the problems exactly for the networks larger than several hundreds of nodes. That is why even a small reduction of network size can significantly improve the speed of finding the solution to these problems. We propose a new method for reducing the network size that preserves the optimal partition in terms of modularity score or the clique partitioning objective function. Furthermore, we prove that the optimal partition of the reduced network has the same quality as the optimal partition of the initial network. We also address the cases where a previously proposed method could provide incorrect results. Finally, we evaluate our method by finding the optimal partitions for two sets of networks. Our results show that the proposed method reduces the network size by 40% on average, decreasing the computation time by about 54%.
Links
MUNI/J/0008/2021, interní kód MUName: Digital City
Investor: Masaryk University, MASH JUNIOR - MUNI Award In Science and Humanities JUNIOR
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