AntLP: ant‐based label propagation algorithm for community detection in social networks. Issue 1 (10th January 2020)

• Record Type: Journal Article
• Authors: Hosseini, Razieh; Rezvanian, Alireza
• Is Part Of: CAAI transactions on intelligence technology. Volume 5:Issue 1(2020)
• Page Start: 34
• Page End: 41
• Publication Date: 2020-01-10
• Subjects: ant colony optimisation -- complex networks -- social networking (online); ant‐based label propagation algorithm -- ant colony optimisation -- similarity indices -- community structures -- LPA -- complex social networks -- meta‐heuristic algorithms -- intelligent algorithms -- social network analysis -- community detection algorithms -- social network datasets -- AntLP; C1180 Optimisation techniques -- C7210N Information networks; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0040
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : In social network analysis, community detection is one of the significant tasks to study the structure and characteristics of the networks. In recent years, several intelligent and meta‐heuristic algorithms have been presented for community detection in complex social networks, among them label propagation algorithm (LPA) is one of the fastest algorithms for discovering community structures. However, due to the randomness of the LPA, its performance is not suitable for the general purpose of network analysis. In this study, the authors propose an improved version of the label propagation (called AntLP) algorithm using similarity indices and ant colony optimisation (ACO). The AntLP consists of two steps: in the first step, the algorithm assigns weights for edges of the input network using several similarity indices, and in the second step, the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network. In order to study the performance of the AntLP, several experiments are conducted on some well‐known social network datasets. Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity, normalised mutual information and running time.