A network community detection algorithm via constrained label propagation with maximization of similarity-based modularity

A fast network community detection algorithm, MLPA, was proposed based on constrained label propagation with maximization of similarity-based modularity. The update process was completed via constrained maximization of similarity-based modularity model. The application of model and structural-similarity makes the accuracy high and the result fits in with character of community, which is densely inter-connected and sparsely connected to other parts of the network. Combined with experimental result that most of the vertices can be assigned to the proper community only within five iterations, the algorithm tries to stop the update process when labels reaches stable. Thus it can reduce the running time greatly. MLPA is efficient for detecting communities in large-scale networks without traditional adjacency matrix calculation.