Research On Community Detection Algorithm Based On Complex Network

A complex network is a network structure composed of a huge number of nodes and intricate relationships between nodes.Many systems in the real world exist in the form of complex networks.A lot of research shows that many actual networks have the characteristics of a community structure,that is,the network divides naturally into groups of nodes with dense connections internally and sparser connections between groups.How to efficiently discover communities in large-scale networks has been a hot research topic in recent years.We first introduce the research background and significance of the community detection theory and the current research status of community detection,then we elaborate the community structure measurement function which is widely used in the undirected network community discovery,namely modularity,as well as various community discovery algorithms for module optimization.We propose a brand-new algorithm.This algorithm is inspired by the modularity to define a property of an edge between nodes,that is relation intensity.The stronger the relation intensity is,the closer the relationship between the two nodes corresponding to the edge is,and the greater the probability that they will be in the same community.According to the order of relation intensity,the algorithm tries to merge the communities corresponding to both ends of the edge in order to get the final partition.We compared the effect of the algorithm with Louvain algorithm through network simulation and real network cases.The results show that for the general network,the algorithm has the same level of accuracy as the Louvain algorithm,but the algorithm proposed in this paper has more advantages in terms of speed.Finally,we use the new algorithm to detect and analyze the community of Facebook social network,which embodies its application value.