research-article

Community detection in incomplete information networks

Authors:

Chuan LiAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 341 - 350

https://doi.org/10.1145/2187836.2187883

Published: 16 April 2012 Publication History

Abstract

With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks.

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Cited By

Fei RWan YHu BLi ACui YPeng H(2025)Deep core node information embedding on networks with missing edges for community detectionInformation Sciences10.1016/j.ins.2025.122039(122039)Online publication date: Feb-2025
https://doi.org/10.1016/j.ins.2025.122039
Chen DNie MXie FWang DChen H(2024)Link Prediction and Graph Structure Estimation for Community DetectionMathematics10.3390/math1208126912:8(1269)Online publication date: 22-Apr-2024
https://doi.org/10.3390/math12081269
Herrmannova DKnoth PStahl CPatton RWells J(2023)Research Collaboration Analysis Using Text and Graph FeaturesComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-23804-8_33(431-441)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-23804-8_33
Show More Cited By

Index Terms

Community detection in incomplete information networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 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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Univ. de Lyon: Universite de Lyon

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Fei RWan YHu BLi ACui YPeng H(2025)Deep core node information embedding on networks with missing edges for community detectionInformation Sciences10.1016/j.ins.2025.122039(122039)Online publication date: Feb-2025
https://doi.org/10.1016/j.ins.2025.122039
Chen DNie MXie FWang DChen H(2024)Link Prediction and Graph Structure Estimation for Community DetectionMathematics10.3390/math1208126912:8(1269)Online publication date: 22-Apr-2024
https://doi.org/10.3390/math12081269
Herrmannova DKnoth PStahl CPatton RWells J(2023)Research Collaboration Analysis Using Text and Graph FeaturesComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-23804-8_33(431-441)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-23804-8_33
Qing H(2022)A Useful Criterion on Studying Consistent Estimation in Community DetectionEntropy10.3390/e2408109824:8(1098)Online publication date: 9-Aug-2022
https://doi.org/10.3390/e24081098
Rahmanian AAli-Eldin ASkubic BElmroth E(2022)MicroSplit: Efficient Splitting of Microservices on Edge Clouds2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)10.1109/SEC54971.2022.00027(252-264)Online publication date: Dec-2022
https://doi.org/10.1109/SEC54971.2022.00027
Jin DYu ZJiao PPan SHe DWu JYu PZhang W(2021)A Survey of Community Detection Approaches: From Statistical Modeling to Deep LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3104155(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3104155
Lyu CShi YSun L(2021)A Novel Local Community Detection Method Using Evolutionary ComputationIEEE Transactions on Cybernetics10.1109/TCYB.2019.293304151:6(3348-3360)Online publication date: Jun-2021
https://doi.org/10.1109/TCYB.2019.2933041
Al-Andoli MTan SCheah WTan S(2021)A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic AlgorithmsIEEE Access10.1109/ACCESS.2021.30953359(96501-96527)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3095335
Zhang BHuang GZheng ZRen JHu C(2018)Approach to Mine the Modularity of Software Network Based on the Most Vital NodesIEEE Access10.1109/ACCESS.2018.28408386(32543-32553)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2840838
Jiang XLi CDing HSun J(2017)Predicting edge sign and finding prestige of nodes in networksCluster Computing10.5555/3110402.311044520:2(1473-1481)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.5555/3110402.3110445
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