Abstract
Constrained community detection is a kind of community detection taking given constraints into account to improve the accuracy of community detection. Optimizing constrained Hamiltonian is one of the methods for constrained community detection. Constrained Hamiltonian consists of Hamiltonian which is generalized modularity and constrained term which takes given constraints into account. Nakata proposed a method for constrained community detection in monoplex networks based on the optimization of constrained Hamiltonian by extended Louvain method.
In this paper, we propose a new method for constrained community detection in multiplex networks. Multiplex networks are the combinations of multiple individual networks. They can represent temporal networks or networks with several types of edges. While optimizing modularity proposed by Mucha et al. is popular for community detection in multiplex networks, our method optimizes the constrained Hamiltonian which we extend for multiplex networks. By using our proposed method, we successfully detect communities taking constraints into account. We also successfully improve the accuracy of community detection by using our method iteratively. Our method enables us to carry out constrained community detection interactively in multiplex networks.
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Acknowledgement
This work was supported by Tokyo Tech - Fuji Xerox Cooperative Research (Project Code KY260195), JSPS Grant-in-Aid for Scientific Research(B) (Grant Number 17H01785) and JST CREST (Grant Number JPMJCR1687).
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Eguchi, K., Murata, T. (2017). Constrained Community Detection in Multiplex Networks. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_6
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DOI: https://doi.org/10.1007/978-3-319-67217-5_6
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