Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the 10th international conference on World Wide Web
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Learning block importance models for web pages
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
A study on combination of block importance and relevance to estimate page relevance
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting a website's content structure from its link structure
Proceedings of the 14th ACM international conference on Information and knowledge management
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Link analysis is one of the most effective methods of web structure mining. Traditional link analysis methods only consider the flat structure of the web with hyperlinks. It may affect the precision of the analysis result inevitably. It is observed that the web could be treated as an entity with a three-layer structure: host layer, page layer and block layer. Considering this three-layer structure is expected to improve the precision significantly when performing link analysis. A novel algorithm, three-layer based ranking is proposed to complete this task. In this algorithm, the important hosts and blocks are found within adaptations of the traditional link analysis methods. Based on these hosts and blocks, the web pages both belonged to important hosts and containing important blocks could be retrieved. These web pages are just the authoritative web pages that link analysis is looking for. We experimentally evaluate the precision of our three-layer based ranking algorithm. It is concluded from extensive experiments that this method outperforms other traditional algorithms significantly.