YuChing Lu^{1}, Goutam Chakraborty^{2*}, Masafumi Matsuhara^{3}
^{1 }Graduate School of Software Information Science, Iwate Prefectural University, Iwate, Japan
^{2 }Graduate School of Software Information Science, Iwate Prefectural University, Iwate, Japan, Sendai Foundation of Applied Information Sciences, Japan
^{3 }Graduate School of Software Information Science, Iwate Prefectural University, Iwate, Japan
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ABSTRACT
Graph clustering is a classical problem, and is proven to be NPcomplete. It is at the core of many useful algorithms, like Network and VLSI design, computer graphics, data mining etc. In recent years, with exponential increase in the use of social network and strong incentive for creating applications exploiting the information hidden in these networks, clustering of large graphs (social networks) has become an important research topic. As the problem is NPcomplete, various heuristic algorithms are proposed to find near optimal solutions efficiently. Optimization criteria are defined depending on the applications. Two important criteria for all heuristic algorithms are quality of the result and its stability over different runs on the same problem. In this work, we proposed a two stage genetic algorithm for network clustering. Modularity index for the partitioned graph is the criterion to optimize. By experimenting with several reallife networks, we have shown that our algorithm is stable and delivers a high modularity partitioning compared to other competitive heuristic algorithms. The stability of the algorithm is analyzed through simulations.
Keywords:
Graph clustering, Social network analysis, Multiobjective optimization, Genetic algorithm.
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ARTICLE INFORMATION
Received:
20200425
Revised:
20200706
Accepted:
20200827
Available Online:
20200901
Cite this article:
Lu, Y.C., Chakraborty, G., Matsuhara, M. 2020. A two stage converging genetic algorithm for graph clustering. International Journal of Applied Science and Engineering, 17, 299–310. https://doi.org/10.6703/IJASE.202009_17(3).299
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.