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Uncovering communities in multidimensional networks with multiobjective genetic algorithms

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 75 - 76

https://doi.org/10.1145/2598394.2598453

Published: 12 July 2014 Publication History

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Abstract

A framework for community discovery in multidimensional networks based on an evolutionary approach is proposed. Each network is clustered by running a multiobjective genetic algorithm that tries to maximize the modularity function of the current network and, at the same time, to minimize the difference between the current community structure and that obtained on the already considered dimensions. Experiments on synthetic datasets show the capability of the approach in discovering latent shared group organization of individuals.

References

[1]

D. Chakrabarti, R. Kumar, and A. Tomkins. Evolutionary clustering. In Proc. of the 12th ACM International Conference on Knowledge Discovery and Data Mining (KDD'06), pages 554--560, 2006.

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Google Scholar

[2]

F. Folino and C. Pizzuti. An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Transactions on Knowledge and Data Engineering, to appear, 2014.

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[3]

M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review, E69:026113, 2004.

Google Scholar

[4]

L. Tang, X. Wang, and H. Liu. Uncoverning groups via heterogeneous interaction analysis. In The Ninth IEEE International Conference on Data Mining (ICDM'09), pages 503--512, 2009.

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Karimi-Majd AFathian MMakrehchi M(2018)Consensus-based methodology for detection communities in multilayered networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2017.11.130494(547-558)Online publication date: Mar-2018
https://doi.org/10.1016/j.physa.2017.11.130
Karimi‐Majd AFathian M(2017)Multiobjective approach for detecting communities in heterogeneous networksComputational Intelligence10.1111/coin.1213733:4(980-1004)Online publication date: 9-Aug-2017
https://doi.org/10.1111/coin.12137
Kuncheva ZMontana G(2015)Community Detection in Multiplex Networks using Locally Adaptive Random WalksProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201510.1145/2808797.2808852(1308-1315)Online publication date: 25-Aug-2015
https://dl.acm.org/doi/10.1145/2808797.2808852

Index Terms

Uncovering communities in multidimensional networks with multiobjective genetic algorithms
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Karimi-Majd AFathian MMakrehchi M(2018)Consensus-based methodology for detection communities in multilayered networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2017.11.130494(547-558)Online publication date: Mar-2018
https://doi.org/10.1016/j.physa.2017.11.130
Karimi‐Majd AFathian M(2017)Multiobjective approach for detecting communities in heterogeneous networksComputational Intelligence10.1111/coin.1213733:4(980-1004)Online publication date: 9-Aug-2017
https://doi.org/10.1111/coin.12137
Kuncheva ZMontana G(2015)Community Detection in Multiplex Networks using Locally Adaptive Random WalksProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201510.1145/2808797.2808852(1308-1315)Online publication date: 25-Aug-2015
https://dl.acm.org/doi/10.1145/2808797.2808852

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A new multiobjective genetic algorithm with heterogeneous population for solving flowshop scheduling problems

Mesoscopic analysis of networks with genetic algorithms

Adaptive community detection in complex networks using genetic algorithms

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