title: Detecting Community Structure by Using a Constrained Label Propagation Algorithm.
abstract: Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks gained immense interest over the last decade. A lot of community detection methods were proposed, and one of them is the label propagation algorithm (LPA). The simplicity and time efficiency of the LPA make it a popular community detection method. However, the LPA suffers from instability detection due to randomness that is induced in the algorithm. The focus of this paper is to improve the stability and accuracy of the LPA, while retaining its simplicity. Our proposed algorithm will first detect the main communities in a network by using the number of mutual neighbouring nodes. Subsequently, nodes are added into communities by using a constrained LPA. Those constraints are then gradually relaxed until all nodes are assigned into groups. In order to refine the quality of the detected communities, nodes in communities can be switched to another community or removed from their current communities at various stages of the algorithm. We evaluated our algorithm on three types of benchmark networks, namely the Lancichinetti-Fortunato-Radicchi (LFR), Relaxed Caveman (RC) and Girvan-Newman (GN) benchmarks. We also apply the present algorithm to some real-world networks of various sizes. The current results show some promising potential, of the proposed algorithm, in terms of detecting communities accurately. Furthermore, our constrained LPA has a robustness and stability that are significantly better than the simple LPA as it is able to yield deterministic results.
authors.affiliation: Institute of Mathematical Science, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Jia Hou
authors.identifier: 0000-0001-7626-6778
authors.initials: JH
authors.lastname: Chin
authors.affiliation: Institute of Mathematical Science, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Kuru
authors.initials: K
authors.lastname: Ratnavelu
mesh_terms: D000465:Algorithms; D019058:Community Networks; D008962:Models, Theoretical; D012111:Residence Characteristics