The community analysis of user behaviors network for web traffic

Cai, Jun, Yu, Shun-Zheng and Wang, Yu 2011, The community analysis of user behaviors network for web traffic, Journal of software, vol. 6, no. 11, pp. 2217-2224, doi: 10.4304/jsw.6.11.2217-2224.

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Title The community analysis of user behaviors network for web traffic
Author(s) Cai, Jun
Yu, Shun-Zheng
Wang, YuORCID iD for Wang, Yu orcid.org/0000-0002-9807-2293
Journal name Journal of software
Volume number 6
Issue number 11
Start page 2217
End page 2224
Total pages 8
Publisher Academy Publisher
Place of publication Oulu, Finland
Publication date 2011-11
ISSN 1796-217X
Keyword(s) complex networks
user behaviors
community
clustering coefficient
bipartite network
Summary 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.
Language eng
DOI 10.4304/jsw.6.11.2217-2224
Field of Research 0803 Computer Software
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2011, Academy Publisher
Persistent URL http://hdl.handle.net/10536/DRO/DU:30103584

Document type: Journal Article
Collection: School of Information Technology
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