The Research Of Community Detection Based On LT Model And Cooperative Method

Since the21st century, with the development and popularization of information technology, and a variety of popular social networking sites, researchers can easily collect the complex social network datasets, which has largely inspired researchers to study complex social networks. Studies have shown that complex social networks have certain features such as small-world, scale-free and modularity. Generally, community is identified as a set of nodes whose links inside are more densely than links outside. Community detection algorithm which has always been a hot research topic, mainly studies how to find the high-quality of the community structure in complex social network. Good community structure is useful for complex social network analysis.Community detection is the key issue of finding the structure and understanding the function of many natural and artificial systems. In this paper, the community detection algorithm is the main study which is we focused on, we have achieved some results. First, we present a similarity calculation method based on random walks (RWS), which is used to quickly calculate the nodes’ similarity according to the topology and weight information of the network. Second, a community detection algorithm (LTCD) based on the linear threshold model is proposed, which continuously activated the boundary node to extend the initial community. Finally, the paper also proposes a method based on cooperative method (CMCD), which is used to optimize the community structure found by community detection algorithm and improve its stability.In this paper, experiments on some real and artificial synthetic social network datasets prove that three algorithms mentioned above are feasible and can detect high-quality community structure.