research-article

Fast query execution for retrieval models based on path-constrained random walks

Authors:

William W. CohenAuthors Info & Claims

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

Pages 881 - 888

https://doi.org/10.1145/1835804.1835916

Published: 25 July 2010 Publication History

Abstract

Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph, with typed nodes representing documents, terms, and metadata, and labeled edges representing the relationships between them. Recent work has shown that the accuracy of the widely-used random-walk-based proximity measures can be improved by supervised learning - in particular, one especially effective learning technique is based on Path-Constrained Random Walks (PCRW), in which similarity is defined by a learned combination of constrained random walkers, each constrained to follow only a particular sequence of edge labels away from the query nodes. The PCRW based method significantly outperformed unsupervised random walk based queries, and models with learned edge weights. Unfortunately, PCRW query systems are expensive to evaluate. In this study we evaluate the use of approximations to the computation of the PCRW distributions, including fingerprinting, particle filtering, and truncation strategies. In experiments on several recommendation and retrieval problems using two large scientific publications corpora we show speedups of factors of 2 to 100 with little loss in accuracy.

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Cited By

Wang QZhu CZhang YZhong HZhong JSheng V(2022)Short Text Topic Learning Using Heterogeneous Information NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3147766(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3147766
Chen ZShu JLiu L(2022)A Survey of Data Mining Method in Heterogeneous Information Networks with Node Importance Evaluation2022 IEEE 8th International Conference on Computer and Communications (ICCC)10.1109/ICCC56324.2022.10065864(412-417)Online publication date: 9-Dec-2022
https://doi.org/10.1109/ICCC56324.2022.10065864
Wang QLu JLi WZhang JSheng V(2022)Homophily-aware correction framework for crowdsourced labels using heterogeneous information networkExpert Systems with Applications10.1016/j.eswa.2022.116896200(116896)Online publication date: Aug-2022
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Index Terms

Fast query execution for retrieval models based on path-constrained random walks
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

1240 pages

ISBN:9781450300551

DOI:10.1145/1835804

General Chairs:
Bharat Rao
Siemens
,
Balaji Krishnapuram
Siemens
,
Program Chairs:
Andrew Tomkins
Google Inc.
,
Qiang Yang
Hong Kong University of Science and Technology

Copyright © 2010 ACM.

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: 25 July 2010

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KDD '10

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25 - 28, 2010

DC, Washington, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Wang QZhu CZhang YZhong HZhong JSheng V(2022)Short Text Topic Learning Using Heterogeneous Information NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3147766(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3147766
Chen ZShu JLiu L(2022)A Survey of Data Mining Method in Heterogeneous Information Networks with Node Importance Evaluation2022 IEEE 8th International Conference on Computer and Communications (ICCC)10.1109/ICCC56324.2022.10065864(412-417)Online publication date: 9-Dec-2022
https://doi.org/10.1109/ICCC56324.2022.10065864
Wang QLu JLi WZhang JSheng V(2022)Homophily-aware correction framework for crowdsourced labels using heterogeneous information networkExpert Systems with Applications10.1016/j.eswa.2022.116896200(116896)Online publication date: Aug-2022
https://doi.org/10.1016/j.eswa.2022.116896
Yu SWu YGan RZhou JZheng ZXuan Q(2022)Discover Important Paths in the Knowledge Graph Based on Dynamic Relation ConfidenceBig Data and Social Computing10.1007/978-981-19-7532-5_22(341-358)Online publication date: 7-Dec-2022
https://doi.org/10.1007/978-981-19-7532-5_22
Do P(2022)Finding All Shortest Meaningful Meta-Paths Between Two Vertices of a Secured Large Heterogeneous Information Network Using Distributed AlgorithmRobotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities10.1007/978-3-030-96737-6_10(171-192)Online publication date: 29-Mar-2022
https://doi.org/10.1007/978-3-030-96737-6_10
Deng WDu WHan C(2021)Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendationsJournal of Information Science10.1177/0165551521104742349:5(1309-1324)Online publication date: 13-Oct-2021
https://doi.org/10.1177/01655515211047423
Xiao WZhao HZheng VSong YDemartini GZuccon GCulpepper JHuang ZTong H(2021)Neural PathSim for Inductive Similarity Search in Heterogeneous Information NetworksProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482454(2201-2210)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482454
Ramaciotti Morales PLamarche-Perrin RFournier-S'niehotta RPoulain RTabourier LTarissan F(2021)Measuring diversity in heterogeneous information networksTheoretical Computer Science10.1016/j.tcs.2021.01.013859(80-115)Online publication date: Mar-2021
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Do P(2020)Distributed Algorithms for Finding Meta-Paths of a Large Heterogeneous Information Network on CloudModern Principles, Practices, and Algorithms for Cloud Security10.4018/978-1-7998-1082-7.ch011(223-249)Online publication date: 2020
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Wang YWang ZZhao ZLi ZJian XXin HChen LSong JChen ZZhao M(2020)Effective Similarity Search on Heterogeneous Networks: A Meta-path Free ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.3019488(1-1)Online publication date: 2020
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