research-article

Mining Community Structures in Multidimensional Networks

Authors:

Oualid Boutemine,

Mohamed BouguessaAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 4

Article No.: 51, Pages 1 - 36

https://doi.org/10.1145/3080574

Published: 29 June 2017 Publication History

Abstract

We investigate the problem of community detection in multidimensional networks, that is, networks where entities engage in various interaction types (dimensions) simultaneously. While some approaches have been proposed to identify community structures in multidimensional networks, there are a number of problems still to solve. In fact, the majority of the proposed approaches suffer from one or even more of the following limitations: (1) difficulty detecting communities in networks characterized by the presence of many irrelevant dimensions, (2) lack of systematic procedures to explicitly identify the relevant dimensions of each community, and (3) dependence on a set of user-supplied parameters, including the number of communities, that require a proper tuning. Most of the existing approaches are inadequate for dealing with these three issues in a unified framework. In this paper, we develop a novel approach that is capable of addressing the aforementioned limitations in a single framework. The proposed approach allows automated identification of communities and their sub-dimensional spaces using a novel objective function and a constrained label propagation-based optimization strategy. By leveraging the relevance of dimensions at the node level, the strategy aims to maximize the number of relevant within-community links while keeping track of the most relevant dimensions. A notable feature of the proposed approach is that it is able to automatically identify low dimensional community structures embedded in a high dimensional space. Experiments on synthetic and real multidimensional networks illustrate the suitability of the new method.

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Cited By

Zarei BArasteh BAsadi MMajidnezhad VAfshord SBouyer A(2024)Multiplex Community Detection in Social Networks Using a Chaos-Based Hybrid Evolutionary ApproachComplexity10.1155/2024/10160862024Online publication date: 1-Jan-2024
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Abdous KMrabah NBouguessa M(2024)Hierarchical Aggregations for High-Dimensional Multiplex Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330580936:4(1624-1637)Online publication date: 1-Apr-2024
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Ortiz-Bouza MAviyente S(2024)Community Detection in Multiplex Networks Based on Orthogonal Nonnegative Matrix Tri-FactorizationIEEE Access10.1109/ACCESS.2024.335170912(6423-6436)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351709
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Index Terms

Mining Community Structures in Multidimensional Networks
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 4

Special Issue on KDD 2016 and Regular Papers

November 2017

419 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3119906

Editor:
Jie Tang
Tsinghua University, China

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Copyright © 2017 ACM.

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Publication History

Published: 29 June 2017

Accepted: 01 April 2017

Revised: 01 October 2016

Received: 01 December 2015

Published in TKDD Volume 11, Issue 4

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Cited By

Zarei BArasteh BAsadi MMajidnezhad VAfshord SBouyer A(2024)Multiplex Community Detection in Social Networks Using a Chaos-Based Hybrid Evolutionary ApproachComplexity10.1155/2024/10160862024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/1016086
Abdous KMrabah NBouguessa M(2024)Hierarchical Aggregations for High-Dimensional Multiplex Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330580936:4(1624-1637)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3305809
Ortiz-Bouza MAviyente S(2024)Community Detection in Multiplex Networks Based on Orthogonal Nonnegative Matrix Tri-FactorizationIEEE Access10.1109/ACCESS.2024.335170912(6423-6436)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351709
Liu XChen WChen XChen JCheng L(2024)Could free-floating bikeshare weed out station-based bikeshare? Analyzing the relationship between two bikeshare systems from bivariate flow clusteringJournal of Transport Geography10.1016/j.jtrangeo.2024.103941118(103941)Online publication date: Jun-2024
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Milano MCinaglia PGuzzi PCannataro M(2024)A novel local alignment algorithm for Multilayer networksInformatics in Medicine Unlocked10.1016/j.imu.2023.10142544(101425)Online publication date: 2024
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Milano M(2024)Multilayer Biological Networks: Analysis AlgorithmsReference Module in Life Sciences10.1016/B978-0-323-95502-7.00189-5Online publication date: 2024
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Santra AIrany FMadduri KChakravarthy SBhowmick S(2023)Efficient community detection in multilayer networks using boolean compositionsFrontiers in Big Data10.3389/fdata.2023.11447936Online publication date: 23-Aug-2023
https://doi.org/10.3389/fdata.2023.1144793
Tajeuna EBouguessa MWang S(2023)Modeling Regime Shifts in Multiple Time SeriesACM Transactions on Knowledge Discovery from Data10.1145/359285717:8(1-31)Online publication date: 28-Jun-2023
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Ding FLuo NYu SWang TXia F(2023)MEGA: Explaining Graph Neural Networks with Network Motifs2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191684(1-9)Online publication date: 18-Jun-2023
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Wang BCai XXu MXiang W(2023)A graph-enhanced attention model for community detection in multiplex networksExpert Systems with Applications10.1016/j.eswa.2023.120552230(120552)Online publication date: Nov-2023
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