Semi-Supervised Community Discovery Algorithm Based on Node Similarity

With the advent of the era 01 big data, complex network community detection has become an important research direction. Based on the similarity of the community detection methods attractions GN algorithm fast and accurate but has higher time complexity. In order to overcome the deficiency of GN efficiency, this paper presents a semi-supervised GN algorithm based on node similarity, takes full advantage of the known node, cannot link constraints, a priori information combined with the similarity information between nodes, and validated using artificial and real networks. It is proved that the algorithm proposed in this paper reduces the GN algorithm's time complexity and improve the efficiency.