关键词: 动态距离, 模糊社区, 社区识别, 网络分析

Abstract: Community identification technology is the basis for the early warning and prediction of potential harmful behaviors in the public security field and the traceability of harmful behaviors. The traditional community identification algorithm treats the community as a single set and cannot describe the problems of the community’s primary and secondary members. This paper proposes a fuzzy community recognition algorithm based on dynamic distance. The algorithm divides the traditional single community structure into core areas and marginal areas, and describes the fuzzy intervals of the community by marginal areas. The algorithm conceives the network as a dynamic evolution model. Any node in the network will interact with other nodes. The interaction will change the distance between nodes, and the distance will in turn affect the interaction. Under the definition of the threshold, the nodes attracted by multiple communities are divided into marginal regions, and the final distance distribution tends to be stable, and the various community structures are revealed. The effectiveness of CDFDD algorithm in community identification is verified by comparative experiments.

Key words: dynamic distance, fuzzy community, community detection, network analysis