Title :
How Contents Influence Clustering Features in the Web
Author :
Cheng, Xueqi ; Ren, Fuxin ; Cao, Xianbin ; Ma, Jing
Abstract :
In World Wide Web, contents of web documents play important roles in the evolution process because of their effects on linking preference. A majority of topological properties are content-related, and among them the clustering features are sensitive to contents of Web documents. In this paper, we first observe the impacts of content similarity on web links by introducing a metric called Linkage Probability. Then we investigate how contents influence the formation mechanism of the most basic cluster, triangle, with a metric named Triangularization Probability. Experimental results indicate that content similarity has a positive function in the process of cluster formation in theWeb. Theoretical analysis predicts the contents influence on the clustering features in the Web very well.
Keywords :
Complex networks; Computer science; Couplings; Information retrieval; Joining processes; Web pages; Web sites;
Conference_Titel :
Web Intelligence, IEEE/WIC/ACM International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3026-0