Abstract
Community detection is an important problem in network clustering. This paper proposes a new network clustering method based on the control of cluster size. The word “cluster size" refers to the number of objects in a cluster. The optimization problem of the proposed method considers a constraint on the number of objects classified into a cluster. The proposed method accurately detects the community structure from the network data by adjusting the lower and upper limits of the cluster size as the parameters. Numerical experiments were conducted using two artificial and six benchmark datasets to verify the effectiveness of the proposed method. In the numerical experiments, the proposed method was compared with the k-medoids clustering, the Louvain method, and spectral clustering. The results show that the proposed method yields better results in terms of both clustering performance and community detection than the conventional methods.
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This work was partly supported by JSPS KAKENHI Grant Number JP19K12146.
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Hamasuna, Y., Nakano, S., Endo, Y. (2021). Network Clustering with Controlled Node Size. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2021. Lecture Notes in Computer Science(), vol 12898. Springer, Cham. https://doi.org/10.1007/978-3-030-85529-1_20
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