The Research On Community Detection Algorithm Based On Fuzzy Mathematics

Complex networks can model many natural phenomena such as common human relationships,computer networks,and social relationships.At present,the research of complex networks has received unprecedented attention.The research results of complex networks have been applied to many fields,especially for the development of e-commerce and social software.As a very important research direction in the field of complex networks,community detection is to classify all nodes in a complex network into multiple communities.Community detection can help us to understand the relationships among all nodes in a complex network,and discover community structures in complex networks to better provide technologies for various industries and applications such as e-commerce,medicine,finance,social networking,transportation,and power supply.Support and services.This paper proposes a community detection algorithm based on fuzzy clustering analysis in fuzzy mathematics.The algorithm is to form a plurality of communities after initializing a complex network,and then use a fuzzy clustering analysis method to merge multiple communities to form the final community.The main work of this paper is as follows:1.An initial community algorithm based on node density is used to divide all the nodes in the network into communities,and the entire network is divided into a set of unit communities and a set of isolated nodes;2.Using fuzzy clustering analysis method or local modularity method,the unit community collections are classified and merged to form a community collection,and the isolated node is then merged with the local modularity method and the node influence method until all the nodes are assigned to the community;3.Implement a community detection algorithm based on fuzzy mathematics,and verify the algorithm with four real data.The community detection algorithm based on fuzzy mathematics proposed in this paper has a good effect on the community division of real network data.The research done in this paper provides a new idea and direction for the further development of related theories of community detection.It has important practical significance and wide application prospects.