Evolving to recognize high-dimensional relationships in data: GA operators and representation designed expressly for community detection
Pages 308 - 309
Abstract
We present a new algorithm for network clustering based upon genetic algorithm methods to optimize modularity. The algorithm proposes an innovative, more abstract representation, along with newly designed domain-specific genetic operators. We then analyze the performance of the algorithm using popular real-world data sets taken from multiple domains. The analysis demonstrates that our algorithm consistently finds high quality or even optimal solutions without any a priori knowledge of the network or the desired number of clusters. Furthermore, we compare our results with five previously published methods and yield the highest quality for the largest of the benchmark datasets.
References
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Aloise, D. and Caporossi, G. 2012. Modularity maximization in networks by variable neighborhood search. 10th DIMACS Implementation Challenge - Graph Partitioning and Graph Clustering. (2012).
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Bilal, S. and Abdelouahab, M. 2017. Evolutionary algorithm and modularity for detecting communities in networks. Physica A: Statistical Mechanics and its Applications. 473, (2017), 89--96.
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Blondel, V.D. et al. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. 2008, 10 (2008), 1--12.
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Guerrero, M. et al. 2017. Adaptive community detection in complex networks using genetic algorithms. Neurocomputing. 266, (2017), 101--113.
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Liu, D. et al. 2013. Genetic Algorithm with a Local Search Strategy for Discovering Communities in Complex Networks. International Journal of Computational Intelligence Systems. 6, 2 (2013), 354--369.
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Liu, H. et al. 2016. Genetic algorithm optimizing modularity for community detection in complex networks. Chinese Control Conference, CCC. 2016--Augus, 1 (2016), 1252--1256.
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Rao, A. et al. 2018. Efficient Reduced-Bias Genetic Algorithm (ERBGA) for Generic Community Detection Objectives. MWAIS 2018 Proceedings. 32 (May 2018).
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Index Terms
- Evolving to recognize high-dimensional relationships in data: GA operators and representation designed expressly for community detection
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Published In
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
- Editor:
- Manuel López-Ibáñez,
- General Chairs:
- Anne Auger,
- Thomas Stützle
Copyright © 2019 ACM.
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Published: 13 July 2019
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