Abstract：The scientific definition of the local similarity index is essential for the algorithm of community detection based on local similarity. The local similarity indexes based on common neighbors underestimate the similarity value of neighbor nodes,The correlation information of node pairs is involved in the definition of local similarity index,network nodes are clustered by this similarity measure combining with K-means spectral clustering. The similarity index proposed by the paper overcomes the shortcomings of traditional local similarity index, and maintains the original computational complexity .The proposed method is tested on both computer-generated and real-world networks, and is compared with the typical algorithms in community detection. Experimental results verify and confirm the feasibility and validity of the proposed method.
