Complex Network Community Structure of User Behaviors and Its Statistical Characteristics

Understanding the structure and dynamics of the user behavior networks that connect users with servers across the Internet is a key to modeling the network and designing future application. In this paper, we obtained the result that the out-degree distribution of clients (the host initiating the connection), the in-degree distribution of servers (the host receiving the connection) are approximately power-law. The clustering coefficient of clients and servers is larger than that in randomized, degree preserving versions of the same graph. Finally, based on the algorithm of finding the community structure in bipartite network, we divided the clients into different communities, through manual examination of hosts in these communities, the typical normal (interest) and abnormal (DOS) communities were found. The structure analysis of the user behavior networks is very helpful for the network management, resource allocation, traffic engineering and security.