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Node ranking in labeled directed graphs

Published: 13 November 2004 Publication History

Abstract

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 <i>PageRank</i>.
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 <b>www</b> (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 <i>PageRank</i>. 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.

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  1. Node ranking in labeled directed graphs

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      cover image ACM Conferences
      CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management
      November 2004
      678 pages
      ISBN:1581138741
      DOI:10.1145/1031171
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 13 November 2004

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      Author Tags

      1. citation graph
      2. context-sensitive ranking
      3. flow-based
      4. intranet search
      5. link structure
      6. model
      7. pagerank
      8. random surfer model
      9. search
      10. search in context
      11. web graph

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      CIKM04: Conference on Information and Knowledge Management
      November 8 - 13, 2004
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      • (2014)STUN: querying spatio-temporal uncertain (social) networksSocial Network Analysis and Mining10.1007/s13278-014-0156-x4:1Online publication date: 5-Feb-2014
      • (2010)Outcome aware ranking in interaction networksProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871470(229-238)Online publication date: 26-Oct-2010
      • (2006)Labeled Link Analysis for Extracting User Characteristics in E-commerce Activity NetworkProceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence10.1109/WI.2006.108(73-80)Online publication date: 18-Dec-2006
      • (2006)Network Flow for Collaborative RankingKnowledge Discovery in Databases: PKDD 200610.1007/11871637_41(434-445)Online publication date: 2006

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