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Discovering overlapping communities using a new diffusion approach based on core expanding and local depth traveling in social networks
(Taylor & Francis Ltd, 2023) Bouyer, Asgarali; Sabavand Monfared, Maryam; Nourani, Esmaeil; Arasteh, Bahman
This paper proposes a local diffusion-based approach to find overlapping communities in social networks based on label expansion using local depth first search and social influence information of nodes, called the LDLF algorithm. It is vital to start the diffusion process in local depth, traveling from specific core nodes based on their local topological features and strategic position for spreading community labels. Correspondingly, to avoid assigning excessive and unessential labels, the LDLF algorithm prudently removes redundant and less frequent labels for nodes with multiple labels. Finally, the proposed method finalizes the node's label based on the Hub Depressed index. Thanks to requiring only two iterations for label updating, the proposed LDLF algorithm runs in low time complexity while eliminating random behavior and achieving acceptable accuracy in finding overlapping communities for large-scale networks. The experiments on benchmark networks prove the effectiveness of the LDLF method compared to state-of-the-art approaches.