research-article

Fast computation of SimRank for static and dynamic information networks

Authors:

Tianyi WuAuthors Info & Claims

EDBT '10: Proceedings of the 13th International Conference on Extending Database Technology

Pages 465 - 476

https://doi.org/10.1145/1739041.1739098

Published: 22 March 2010 Publication History

Abstract

Information networks are ubiquitous in many applications and analysis on such networks has attracted significant attention in the academic communities. One of the most important aspects of information network analysis is to measure similarity between nodes in a network. SimRank is a simple and influential measure of this kind, based on a solid theoretical "random surfer" model. Existing work computes SimRank similarity scores in an iterative mode. We argue that the iterative method can be infeasible and inefficient when, as in many real-world scenarios, the networks change dynamically and frequently. We envision non-iterative method to bridge the gap. It allows users not only to update the similarity scores incrementally, but also to derive similarity scores for an arbitrary subset of nodes. To enable the non-iterative computation, we propose to rewrite the SimRank equation into a non-iterative form by using the Kronecker product and vectorization operators. Based on this, we develop a family of novel approximate SimRank computation algorithms for static and dynamic information networks, and give their corresponding theoretical justification and analysis. The non-iterative method supports efficient processing of various node analysis including similarity tracking and centrality tracking on evolving information networks. The effectiveness and efficiency of our proposed methods are evaluated on synthetic and real data sets.

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cover image ACM Other conferences

EDBT '10: Proceedings of the 13th International Conference on Extending Database Technology

March 2010

741 pages

ISBN:9781605589459

DOI:10.1145/1739041

Editors:
Ioana Manolescu
INRIA, France
,
Stefano Spaccapietra
EPFL, Switzerland
,
Jens Teubner
ETH Zurich, Switzerland
,
Masaru Kitsuregawa
Tokyo University, Japan
,
Alain Leger
Orange - France Telecom R&D, France
,
Felix Naumann
Hasso Plattner Institute, Germany
,
Anastasia Ailamaki
EPFL, Switzerland
,
Fatma Ozcan
IBM Almaden Research Center

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: 22 March 2010

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EDBT/ICDT '10

EDBT/ICDT '10: EDBT/ICDT '10 joint conference

March 22 - 26, 2010

Lausanne, Switzerland

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

Zheng YQu JYang J(2024)StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information NetworksApplied Sciences10.3390/app1402093514:2(935)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020935
Gui C(2024)Link prediction based on spectral analysisPLOS ONE10.1371/journal.pone.028738519:1(e0287385)Online publication date: 2-Jan-2024
https://doi.org/10.1371/journal.pone.0287385
Xu XYu WChua TNgo CKumar RLauw HKa-Wei Lee R(2024)I-CoSim: Efficient Dynamic CoSimRank Retrieval on Evolving NetworksCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651523(923-926)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651523
Yajnik DAnsari HNijhara PBanerjee D(2024)SimRank on Data Streams2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)10.1109/HiPCW63042.2024.00046(137-138)Online publication date: 18-Dec-2024
https://doi.org/10.1109/HiPCW63042.2024.00046
Zhang CHong XZheng Y(2024)Fast computation of General SimRank on heterogeneous information networkDiscover Computing10.1007/s10791-024-09438-527:1Online publication date: 21-May-2024
https://doi.org/10.1007/s10791-024-09438-5
Ge QLiu YZhao YSun YZou LChen YPan A(2023)Efficient and Accurate SimRank-Based Similarity Joins: Experiments, Analysis, and ImprovementProceedings of the VLDB Endowment10.14778/3636218.363621917:4(617-629)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636219
Zhang MXiao YWang WSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Efficient Single-Source SimRank Query by Path AggregationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599328(3342-3352)Online publication date: 4-Aug-2023
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Li ZFu DHe J(2023)Everything Evolves in Personalized PageRankProceedings of the ACM Web Conference 202310.1145/3543507.3583474(3342-3352)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583474
Zhang LLi CLuo CChen H(2023)All-Pairs SimRank Updates on Dynamic Graphs2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00050(131-138)Online publication date: 21-Dec-2023
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Zhang LLi CZhang XChen H(2023)Hierarchical All-Pairs SimRank CalculationDatabase Systems for Advanced Applications10.1007/978-3-031-30675-4_17(252-268)Online publication date: 15-Apr-2023
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