WebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks

Abstract

To effectively score pages with uncertainty in web social networks, we first proposed a new concept called transition probability matrix and formally defined the uncertainty in web social networks. Second, we proposed a hybrid page scoring algorithm, called WebScore, based on the PageRank algorithm and three centrality measures including degree, betweenness, and closeness. Particularly, WebScore takes into a full consideration of the uncertainty of web social networks by computing the transition probability from one page to another. The basic idea of WebScore is to: (1) integrate uncertainty into PageRank in order to accurately rank pages, and (2) apply the centrality measures to calculate the importance of pages in web social networks. In order to verify the performance of WebScore, we developed a web social network analysis system which can partition web pages into distinct groups and score them in an effective fashion. Finally, we conducted extensive experiments on real data and the results show that WebScore is effective at scoring uncertain pages with less time deficiency than PageRank and centrality measures based page scoring algorithms.