The Community Analysis of User Behaviors Network for Web Traffic

摘要

Understanding the structure and dynamics of the user behavior networks for web traffic (To be convenient in next sections, we refer to replace it as UBNWT) that connect users with servers across the Internet is a key to modeling the network and designing future application. The Web-visited bipartite networks, called the user behavioral networks, display a natural bipartite structure: two kinds of nodes coexist with links only between nodes of different types. 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) and the strength distribution (the exchange bytes between clients and servers) are approximately power-law, whose exponential is between 1.7 and 3.4. The clustering coefficient of clients and servers is larger than that in randomized, degree preserving versions of the same graph, which indicate a modular structure of UBNWT. 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. Interestingly, the loyalty of clients belonging to the same community in different time is higher than 80%. The structure analysis of UBNWT is very helpful for the network management, resource allocation, traffic engineering and security.