基于快速近邻传播的半监督社区发现算法

Nowadays time efficiency of most of the community detection algorithms is slow, and they can't make use of prior knowledge community effectively, we propose a Fast Semi-supervised Affinity Propagation community detection algorithm(FSAP). First, the algorithm has introduced the pair-wise constraints, Must-link and Cannot-link, to adjust the similarity matrix; Then, according to rule of information passing between the nodes based on the factor graph model of AP, it directly assigns the two nodes with 0 similarity to different cluster to improve time efficiency. Because social networks are usually large-scale sparse network, it has a great deal of pair-wise nodes with 0 similarity, so the algorithm can improve the efficiency in community detection. Compare with other algorithms, the experimental results demonstrate the algorithm has low time cost, and can use prior knowledge to improve the clustering performance effectively.