Article

Mining scale-free networks using geodesic clustering

Authors:

Michael Garland,

Jiawei HanAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 719 - 724

https://doi.org/10.1145/1014052.1014146

Published: 22 August 2004 Publication History

Abstract

Many real-world graphs have been shown to be scale-free---vertex degrees follow power law distributions, vertices tend to cluster, and the average length of all shortest paths is small. We present a new model for understanding scale-free networks based on multilevel geodesic approximation, using a new data structure called a multilevel mesh.Using this multilevel framework, we propose a new kind of graph clustering for data reduction of very large graph systems such as social, biological, or electronic networks. Finally, we apply our algorithms to real-world social networks and protein interaction graphs to show that they can reveal knowledge embedded in underlying graph structures. We also demonstrate how our data structures can be used to quickly answer approximate distance and shortest path queries on scale-free networks.

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Cited By

Chen RLin YYan HLiu JLiu YLi Y(2024)Scaling law of real traffic jams under varying travel demandEPJ Data Science10.1140/epjds/s13688-024-00471-413:1Online publication date: 11-Apr-2024
https://doi.org/10.1140/epjds/s13688-024-00471-4
Toupance PChopard BLefèvre L(2021)Detection of Topology Changes in Dynamical System: An Information Theoretic ApproachCellular Automata10.1007/978-3-030-69480-7_4(26-35)Online publication date: 13-Feb-2021
https://doi.org/10.1007/978-3-030-69480-7_4
Yang LChen ZFeng YLiao ZHu ZLong J(2020)WLeidenRDF: RDF Data Query Method based on Semantic-Enhanced Graph-Clustering Algorithm2020 International Symposium on Theoretical Aspects of Software Engineering (TASE)10.1109/TASE49443.2020.00014(33-40)Online publication date: Dec-2020
https://doi.org/10.1109/TASE49443.2020.00014
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Index Terms

Mining scale-free networks using geodesic clustering
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

Copyright © 2004 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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Publication History

Published: 22 August 2004

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August 22 - 25, 2004

WA, Seattle, USA

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

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Cited By

Chen RLin YYan HLiu JLiu YLi Y(2024)Scaling law of real traffic jams under varying travel demandEPJ Data Science10.1140/epjds/s13688-024-00471-413:1Online publication date: 11-Apr-2024
https://doi.org/10.1140/epjds/s13688-024-00471-4
Toupance PChopard BLefèvre L(2021)Detection of Topology Changes in Dynamical System: An Information Theoretic ApproachCellular Automata10.1007/978-3-030-69480-7_4(26-35)Online publication date: 13-Feb-2021
https://doi.org/10.1007/978-3-030-69480-7_4
Yang LChen ZFeng YLiao ZHu ZLong J(2020)WLeidenRDF: RDF Data Query Method based on Semantic-Enhanced Graph-Clustering Algorithm2020 International Symposium on Theoretical Aspects of Software Engineering (TASE)10.1109/TASE49443.2020.00014(33-40)Online publication date: Dec-2020
https://doi.org/10.1109/TASE49443.2020.00014
Eddamiri SZemmouri EBenghabrit A(2019)An improved RDF data Clustering AlgorithmProcedia Computer Science10.1016/j.procs.2019.01.038148(208-217)Online publication date: 2019
https://doi.org/10.1016/j.procs.2019.01.038
Ferraro MGiordani P(2019)A review and proposal of (fuzzy) clustering for nonlinearly separable dataInternational Journal of Approximate Reasoning10.1016/j.ijar.2019.09.004Online publication date: Sep-2019
https://doi.org/10.1016/j.ijar.2019.09.004
Yuan HHan YCai NAn W(2018)A Multi-Granularity Backbone Network Extraction Method Based on the Topology PotentialComplexity10.1155/2018/86041322018(1-8)Online publication date: 22-Oct-2018
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Liu SZhao QLi JRao W(2017)Graph Summarization Based on Attribute-Connected NetworkWeb and Big Data10.1007/978-3-319-69781-9_16(161-171)Online publication date: 8-Nov-2017
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Hutair MAghbari ZKamel IAnjum AZhao X(2016)Social community detection based on node distance and interestProceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies10.1145/3006299.3006342(274-289)Online publication date: 6-Dec-2016
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Ba-Hutair MAghbari ZKamel I(2016)On detecting communities in social networks with interests2016 12th International Conference on Innovations in Information Technology (IIT)10.1109/INNOVATIONS.2016.7880053(1-5)Online publication date: Nov-2016
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Bei YLin ZChen D(2016)Summarizing scale-free networks based on virtual and real linksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2015.08.048444(360-372)Online publication date: Feb-2016
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