Anti‐triangle centrality‐based community detection in complex networks

Community detection has been extensively studied in the past decades largely because of the fact that community exists in various networks such as technological, social and biological networks. Most of the available algorithms, however, only focus on the properties of the vertices, ignoring the roles of the edges. To explore the roles of the edges in the networks for community discovery, the authors introduce the novel edge centrality based on its antitriangle property. To investigate how the edge centrality characterises the community structure, they develop an approach based on the edge antitriangle centrality with the isolated vertex handling strategy (EACH) for community detection. EACH first calculates the edge antitriangle centrality scores for all the edges of a given network and removes the edge with the highest score per iteration until the scores of the remaining edges are all zero. Furthermore, EACH is characterised by being free of the parameters and independent of any additional measures to determine the community structure. To demonstrate the effectiveness of EACH, they compare it with the state‐of‐the art algorithms on both the synthetic networks and the real world networks. The experimental results show that EACH is more accurate and has lower complexity in terms of community discovery and especially it can gain quite inherent and consistent communities with a maximal diameter of four jumps.Inspec keywords: biology computing, complex networks, graph theory, social sciences computingOther keywords: antitriangle centrality‐based community detection, complex networks, technological networks, social networks, biological networks, vertex properties, edge roles, community discovery, antitriangle property, community structure, edge antitriangle centrality, isolated vertex handling strategy, EACH, antitriangle centrality scores, synthetic networks, real world networks