Authors: Jovanović, Đorđe 
Davidović, Tatjana 
Urošević, Dragan 
Jakšić Kruger, Tatjana 
Ramljak, Dušan
Affiliations: Computer Science 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Variable Neighborhood Search Approach to Community Detection Problem
Series/Report no.: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: LNCS 13858
First page: 188
Last page: 199
Conference: International Conference NMA 2022: Numerical Methods and Applications
Issue Date: 1-Jan-2023
Rank: M33
ISBN: 978-3-031-32411-6
ISSN: 0302-9743
DOI: 10.1007/978-3-031-32412-3_17
Community detection on graphs can help people gain insight into the network’s structural organization, and grasp the relationships between network nodes for various types of networks, such as transportation networks, biological networks, electric power networks, social networks, blockchain, etc. The community in the network refers to the subset of nodes that have greater similarity, i.e. have relatively close internal connections. They should also have obvious differences with members from different communities, i.e. relatively sparse external connections. Solving the community detection problem is one of long standing and challenging optimization tasks usually treated by metaheuristic methods. Thus, we address it by basic variable neighborhood search (BVNS) approach using modularity as the score for measuring quality of solutions. The conducted experimental evaluation on well-known benchmark examples revealed the best combination of BVNS parameters. Preliminary results of applying BVNS with thus obtained parameters are competitive in comparison to the state-of-the-art methods from the literature.
Keywords: Metaheuristics | Modularity maximization | Optimization on graphs | Social networks
Publisher: Springer Link

Show full item record

Page view(s)

checked on May 9, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.