The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
A uniform approach to accelerated PageRank computation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Adding the Temporal Dimension to Search " A Case Study in Publication Search
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Beyond PageRank: machine learning for static ranking
Proceedings of the 15th international conference on World Wide Web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Freshness matters: in flowers, food, and web authority
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Ranking on large-scale graphs with rich metadata
Proceedings of the 20th international conference companion on World wide web
Large-scale graph mining and learning for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Recency-sensitive model of web page authority
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Link analysis is a key technology in contemporary web search engines. Most of the previous work on link analysis only used information from one snapshot of web graph. Since commercial search engines crawl the Web periodically, they will naturally obtain time series data of web graphs. The historical information contained in the series of web graphs can be used to improve the performance of link analysis. In this paper, we argue that page importance should be a dynamic quantity, and propose defining page importance as a function of both PageRank of the current web graph and accumulated historical page importance from previous web graphs. Specifically, a novel algorithm named TemporalRank is designed to compute the proposed page importance. We try to use a kinetic model to interpret this page importance and show that it can be regarded as the solution to an ordinary differential equation. Experiments on link analysis using web graph data in five snapshots show that the proposed algorithm can outperform PageRank in many measures, and can effectively filter out newly appeared link spam websites.