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Community Detection in Complex Networks: A Novel Approach Based on Ant Lion Optimizer

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Proceedings of Sixth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 546))

Abstract

The problem of community detection in complex networks has established an increased amount of interest since the past decade. Community detection is a way to discover the structure of network by assembling the nodes into communities. The grouping performed for the communities encompasses denser interconnection between the nodes than community’s intra-connections. In this paper a novel nature-inspired algorithmic approach based on Ant Lion Optimizer for efficiently discovering the communities in large networks is proposed. The proposed algorithm optimizes modularity function and is able to recognize densely linked clusters of nodes having sparse interconnects. The work is tested on Zachary’s Karate Club, Bottlenose Dolphins, Books about US politics and American college football network benchmarks and results are compared with the Ant Colony Optimization (ACO) and Enhanced Firefly algorithm (EFF) approaches. The proposed approach outperforms EFF and ACO for Zachary and Books about US politics and produces results better than ACO for Dolphins and EFF for American Football Club. The conclusion drawn from experimental results illustrates the potential of the methodology to effectively identify the network structure.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar University, Patiala, 147004, India

    Maninder Kaur & Abhay Mahajan

Authors
  1. Maninder Kaur
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  2. Abhay Mahajan
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Corresponding author

Correspondence to Maninder Kaur .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  2. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, National Institute of Technology, Silchar, Silchar, Assam, India

    Kedar Nath Das

  4. School of Mathematics, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India

    Arvind Kumar Lal

  5. School of Mathematics, Thapar University Patiala, Patiala, Punjab, India

    Harish Garg

  6. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya K. Nagar

  7. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

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Kaur, M., Mahajan, A. (2017). Community Detection in Complex Networks: A Novel Approach Based on Ant Lion Optimizer. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_3

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  • DOI: https://doi.org/10.1007/978-981-10-3322-3_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3321-6

  • Online ISBN: 978-981-10-3322-3

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