Research Of Community Detection In Complex Dynamic Network

At present, Community detection is a focus of research and analysis of network.Community information can help people clearly understand the network structure, cognize the similarity between nodes in a network. In a word, the discovery of community structure in a network is of great significance to the cognition and application of complex network.Although, the research of community discovery algorithm for the static network has made many achievements, most of the them exists the problem of high time complexity when they are faced the dynamic network.Label propagation algorithm is a rapid and simple community discovery algorithm.However, this algorithm has shortcomings like low accuracy and high randomness. This paper analyses causes of the faults of original label propagation algorithm and the problems of key nodes in those improved algorithms. Then this paper put forward PR-LPA which is an improved label propagation algorithm. PR-LPA is based on influence model,and use PageRank algorithm to calculate the influence value of all nodes. Each node spread its label to the neighbors, basing on the influence model. In view of dynamic network, this paper puts forward D-PR-LPA algorithm. According the existing community structure and those changed nodes, D-PR-LPA algorithm can analysis and make sure those nodes which label need to be updated.Then the algorithm will update those nodes.Experiments show that PR-LPA algorithm can get satisfactory effect. And in facing dynamic network, the D-PR-LPA algorithm can get a very similar results to to PR-LPA,only using relatively less time.