Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
CrimeNet explorer: a framework for criminal network knowledge discovery
ACM Transactions on Information Systems (TOIS)
Analyzing terrorist networks: a case study of the global salafi jihad network
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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Applying the centrality measures from social network analysis to score web pages may well represent the essential role of pages and distribute their authorities in a web social network with complex link structures. To effectively score the pages, we propose a hybrid page scoring algorithm, called WebRank, based on the PageRank algorithm and three centrality measures including degree, betweenness, and closeness. The basis idea of WebRank is that: (1) use PageRank to accurately rank pages, and (2) apply centrality measures to compute the importance of pages in web social networks. In order to evaluate the performance of WebRank, we develop a web social network analysis system which can partition web pages into distinct groups and score them in an effective fashion. Experiments conducted on real data show that WebRank is effective at scoring web pages with less time deficiency than centrality measures based social network analysis algorithm and PageRank.