The quest for correct information on the Web: hyper search engines
Selected papers from the sixth international conference on World Wide Web
Improved algorithms for topic distillation in a hyperlinked environment
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Automatic resource compilation by analyzing hyperlink structure and associated text
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 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
What is this page known for? Computing Web page reputations
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Hyperlink Analysis for the Web
IEEE Internet Computing
Mining the Web's Link Structure
Computer
A new paradigm for ranking pages on the world wide web
WWW '03 Proceedings of the 12th international conference on World Wide Web
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Grammar-based geodesics in semantic networks
Knowledge-Based Systems
Outcome aware ranking in interaction networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Network flow for collaborative ranking
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Our work is motivated by the problem of ranking hyper-linked documents for a given query. Given an arbitrary directed graph with edge and node labels, we present a new flow-based model and an efficient method to dynamically rank the nodes of this graph with respect to any of the original labels. Ranking documents for a given query in a hyper-linked document set and ranking of authors/articles for a given topic in a citation database are some typical applications of our method. We outline the structural conditions that the graph must satisfy for our ranking to be different from the traditional PageRank. We have built a system using two indices that is capable of dynamically ranking documents for any given query. We validate our system and method using experiments on a few datasets: a crawl of the IBM Intranet (12 million pages), a crawl of the www (30 million pages) and the DBLP citation dataset. We compare our method to existing schemes for topic-biased ranking that require a classifier and the traditional PageRank. In these experiments, we demonstrate that our method is well suited for fine-grained ranking and that our method performs better than the existing schemes. We also demonstrate that our system can obtain an improved ranking with very little impact on query time.