A Community Detection Algorithm Fusing Node Similarity and Label Propagation.

As a scientific research method to reveal the intrinsic functional properties of complex network systems, Community Detection has already become one of the most popular research topics in complex networks. The typical label propagation algorithms are very suitable for large-scale networks due to their approximate linear time complexity. But too many random strategies in the algorithms make it not stable enough. For that reason, this paper proposes a Community Detection Algorithm Fusing Node Similarity and Label Propagation (FNSLP). First, the algorithm preprocesses the neighboring nodes of the seed nodes by node similarity to reduce the kinds of the initial label. Combined with nodes' influence, the label propagation ability is calculated. Then, the label selection of nodes is assisted by an improved label update strategy, which reduces the phenomenon of label oscillation and improves the accuracy and stability of label selection. Experimental results show that in four real networks, the algorithm achieves the maximum Modularity value on 75% of the datasets. In multiple artificial benchmark networks with different mixing parameters, the algorithm's Normalized Mutual Information value reaches the maximum value.