Label propagation algorithm for community discovery based on centrality and common neighbours

We propose a label propagation-based algorithm to extract community structure using a new similarity measure based on centrality and common neighbours. Initially, a distinct label is assigned to each vertex. Along the process, each vertex adopts the label that most of its similar neighbours have. Communities are defined when closely related groups of vertices agree on a single label. Experiments on both real-world and synthetic networks show that our algorithm has better stability than the label propagation algorithm and finds communities with small sizes and indistinct boundaries more effectively than most state-of-the-art community detection methods.