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Multiobjective evolutionary community detection for dynamic networks

Authors:

Francesco Folino,

Clara PizzutiAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 535 - 536

https://doi.org/10.1145/1830483.1830580

Published: 07 July 2010 Publication History

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Abstract

A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolutionary clustering, assuming that abrupt changes of community structure in short time periods are not desirable. The algorithm correctly detects communities and it is shown to be very competitive w.r.t. some state-of-the-art methods.

References

[1]

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

Digital Library

Google Scholar

[2]

Y. Chi, X. Song, D.Zhou, K.Hino, and B.L. Tseng. Evolutionary spectral clustering by incorporating temporal smoothness. In Proc. of 13th Int. Conf. on Knowledge Discovery and Data Mining (KDD'07), pages 153--162, 2007.

Digital Library

Google Scholar

[3]

M. Ehrgott. Multicriteria Optimization. Springer, Berlin, 2nd edition, 2005.

Digital Library

Google Scholar

[4]

M. S. Kim and J. Han. A particle-and-density based evolutionary clustering method for dynamic networks. In Proc. of 35th Int. Conf. on Very Large Data Bases (VLDB'09), pages 622--633, 2009.

Digital Library

Google Scholar

[5]

Y. Lin, S. Zhu, H. Sundaram, and B. L. Tseng. Facetnet: A framework for analyzing communities and their evolutions in dynamic networks. In Proc. of the 17th World Wide Web Conference (WWW'08), pages 685--694, 2008.

Digital Library

Google Scholar

[6]

M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69(2):026113, 2004.

Crossref

Google Scholar

[7]

C. Pizzuti. GA-NET: a genetic algorithm for community detection in social networks. In Proc. of 10th Int. Conf. on Parallel Problem Solving from Nature (PPSN'08), pages 1081--1090, 2008.

Crossref

Google Scholar

[8]

L. Tang, H. Liu, J. Zhang, and Z. Nazeri. Community evolution in dynamic multi-mode networks. In Proc. of 13th Int. Conf. on Knowledge Discovery and Data Mining (KDD'07), pages 677--685, 2007.

Digital Library

Google Scholar

Cited By

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Cazabet RRossetti G(2023)Challenges in Community Discovery on Temporal NetworksTemporal Network Theory10.1007/978-3-031-30399-9_10(185-202)Online publication date: 21-Nov-2023
https://doi.org/10.1007/978-3-031-30399-9_10
Liu XDing NFiumara GDe Meo PFicara A(2022)Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal NetworksApplied Sciences10.3390/app1208379512:8(3795)Online publication date: 9-Apr-2022
https://doi.org/10.3390/app12083795
Wang SLi GHu GWei HPan YPan Z(2020)Community detection in dynamic networks using constraint non-negative matrix factorizationIntelligent Data Analysis10.3233/IDA-18443224:1(119-139)Online publication date: 18-Feb-2020
https://doi.org/10.3233/IDA-184432
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Index Terms

Multiobjective evolutionary community detection for dynamic networks
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2010

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GECCO '10

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SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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

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Cazabet RRossetti G(2023)Challenges in Community Discovery on Temporal NetworksTemporal Network Theory10.1007/978-3-031-30399-9_10(185-202)Online publication date: 21-Nov-2023
https://doi.org/10.1007/978-3-031-30399-9_10
Liu XDing NFiumara GDe Meo PFicara A(2022)Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal NetworksApplied Sciences10.3390/app1208379512:8(3795)Online publication date: 9-Apr-2022
https://doi.org/10.3390/app12083795
Wang SLi GHu GWei HPan YPan Z(2020)Community detection in dynamic networks using constraint non-negative matrix factorizationIntelligent Data Analysis10.3233/IDA-18443224:1(119-139)Online publication date: 18-Feb-2020
https://doi.org/10.3233/IDA-184432
Coppens LDe Venter JMitrović SDe Weerdt J(2020)A Comparative Study of Community Detection Techniques for Large Evolving GraphsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-43823-4_31(368-384)Online publication date: 28-Mar-2020
https://doi.org/10.1007/978-3-030-43823-4_31
Al-Sharoa EAl-khassaweneh MAviyente S(2019)Low-rank Estimation Based Evolutionary Clustering for Community Detection in Temporal NetworksICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2019.8682987(5381-5385)Online publication date: May-2019
https://doi.org/10.1109/ICASSP.2019.8682987
Cherifi HPalla GSzymanski BLu X(2019)On community structure in complex networks: challenges and opportunitiesApplied Network Science10.1007/s41109-019-0238-94:1Online publication date: 16-Dec-2019
https://doi.org/10.1007/s41109-019-0238-9
Cazabet RRossetti G(2019)Challenges in Community Discovery on Temporal NetworksTemporal Network Theory10.1007/978-3-030-23495-9_10(181-197)Online publication date: 30-Oct-2019
https://doi.org/10.1007/978-3-030-23495-9_10
Rossetti GCazabet R(2018)Community Discovery in Dynamic NetworksACM Computing Surveys10.1145/317286751:2(1-37)Online publication date: 20-Feb-2018
https://dl.acm.org/doi/10.1145/3172867
Gong MCai QMa LWang SLei YGong MCai QMa LWang SLei Y(2017)Network Community Discovery with Evolutionary Multi-objective OptimizationComputational Intelligence for Network Structure Analytics10.1007/978-981-10-4558-5_3(73-134)Online publication date: 20-Sep-2017
https://doi.org/10.1007/978-981-10-4558-5_3
Gong MCai QMa LWang SLei YGong MCai QMa LWang SLei Y(2017)IntroductionComputational Intelligence for Network Structure Analytics10.1007/978-981-10-4558-5_1(1-20)Online publication date: 20-Sep-2017
https://doi.org/10.1007/978-981-10-4558-5_1
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A spiderweb model for community detection in dynamic networks

A signal processing perspective to community detection in dynamic networks

Community-based anomaly detection in evolutionary networks

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