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Abstract
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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.
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