MA-Net: A Reliable Memetic Algorithm for Community Detection by Modularity Optimization

  • Leila Moslemi Naeni
  • Regina Berretta
  • Pablo Moscato
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 1)

Abstract

The information that can be transformed in knowledge from data in challenging real-world problems follows the accelerated rate of the advancement of technology in many different fields from biology to sociology. Complex networks are a useful representation of many problems in these domains One of the most important and challenging problems in network analysis lies in detecting community structures. This area of algorithmic research has attracted great attention due to its possible application in many fields. In this study we propose the MA-Net, memetic algorithm to detect communities in network by optimizing modularity value which is fast and reliable in the sense that it consistently produces sound solutions. Experiments using well-known real-world benchmark networks indicate that in comparison with other state-of-the-art algorithms, MA-Net has an outstanding performance on detecting communities.

Keywords

community detection modularity memetic algorithm 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leila Moslemi Naeni
    • 1
    • 2
  • Regina Berretta
    • 1
    • 2
  • Pablo Moscato
    • 1
    • 2
  1. 1.The Priority Research Centre in Bioinformatics, Biomarker Discovery and Information-based MedicineHunter Medical Research InstituteNewcastleAustralia
  2. 2.School of Electrical Engineering and Computer ScienceThe University of NewcastleCallaghanAustralia

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