Detection of Overlapping Community Based on Fuzzy Logic Theory

It is necessary to detect overlapping communities in complex network research tasks, since the actual social network is complex, and many nodes do not fully belong to a certain organization. Therefore, we propose a new algorithm to detect overlapping communities by incorporating the fuzzy logic theory. By calculating and transforming the node similarity, the most closely connected nodes are divided into the corresponding communities by controlling the parameters, and this process generates a preliminary nonoverlapping community structure. The remaining boundary nodes are redistributed according to the degree of membership among the nodes. The nodes that meet certain conditions are identified as overlapping nodes, while the rest of the nodes are divided into the corresponding communities according to their inclinations. In this manner, the communities can be divided and the overlapping nodes can be identified. The proposed algorithm is validated by applying it to three real datasets. The experimental results indicate that the proposed algorithm can successfully find the overlapping nodes by detecting the community structure, which provides guidance for further investigation.