Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 598, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.physa.2022.127359
Keywords
Overlapping community detection; Neutrosophic theory; Multidimensional scaling; Fuzzy c-means
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This paper proposes a new overlapping community detection algorithm based on neutrosophic set theory, which handles uncertainty from the imprecise definition of communities by dealing with boundary and outlier nodes.
Discovering community structure is one of the most intensively studied problems in network science. Many real networks are composed of nodes belonging to multiple communities. In this manuscript, a new overlapping community detection algorithm is proposed based on neutrosophic set (NS) theory. The proposed community detection method manages uncertainty arisen from imprecise definition of communities, by handling boundary and outlier nodes. In the first step, the proposed algorithm calculates the dissimilarity index between each pair of nodes in the network. Then, in order to keep the original distance between nodes as much as possible, the network structure is mapped into a low-dimensional space by multidimensional scaling. Finally, the neutrosophic c means algorithm is employed to find communities in the network. The experimental results show that the proposed algorithm can detect communities on real and artificial datasets effectively and accurately. (C) 2022 Elsevier B.V. All rights reserved.
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