AntLP: ant-based label propagation algorithm for community detection in social networks

• Main Authors: Razieh Hosseini, Alireza Rezvanian
• Format: Article
• Language: English
• Published: Wiley 2019-12-01
• Series: CAAI Transactions on Intelligence Technology
• Subjects: ant colony optimisation; complex networks; social networking (online); ant-based label propagation algorithm; similarity indices; community structures; lpa; complex social networks; meta-heuristic algorithms; intelligent algorithms; social network analysis; community detection algorithms; social network datasets; antlp
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0040

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.