CDRKD: An improved density peak algorithm based on kernel fuzzy measure in the overlapping community detection

Abstract

 Compared with other traditional community discovery algorithms, density peak clustering algorithm is more efficient in getting network structures through clustering. However, DPC needs to contain the distance information between all nodes as sources, so it cannot directly processing the complex network represented by the adjacency matrix. DPC introduces truncation distance when calculating the local density of nodes, which is usually set as a fixed value according to experience, and lacks self-adaptability for different network structures. A feasible solution to those problems is to combined rough set theory and kernel fuzzy similarity measures. In this work, we present overlapping community detection algorithm based on improved rough entropy fusion density peak. The algorithm applied rough set theory to attribute reduction of massive high-dimensional data. Another algorithm defines the similarity of sample points by the inner product between two vectors on the basis of fuzzy partition matrix. Finally, a community detection algorithm based on rough entropy and kernel fuzzy density peaks clustering (CDRKD) has proposed by combining the two algorithms above, we perform an extensive set of experiments to verify the effectiveness and feasibility of the algorithm.