Abstract
There exist problems in study of social network community detection, such as some algorithms detection result having high time complexity with comparatively satisfactory, existing fast algorithms in low quality because of stochastic iteration partition results for large scale network, and lacking of model and mechanism of individual and link attributes expressing and utilizing. To solve these problems, this paper proposes a recursive merged community detection model based on node cluster, which can express the tightness of relationship between individuals according to the closed preconditions. Based on this, an effective community detection algorithm is designed and implemented. The proposed recursive merging model has high generality and is applicable to both weighted and non-weighted networks. A series of experiments show that the proposed algorithm based on node cluster recursive model and following linked list is effective for community detection in social networks with relatively less time cost. The algorithm can also be applied to the need to fuse integrate individuals and links attributes of community detection algorithm with a comparatively fast speed and high quality partition.
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This work was supported in part by the China Scholarship Council and the National Natural Science Foundation of China under Grant No. 61472272.
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Wang, A., Meng, L., Cui, L. (2021). Recursive Merged Community Detection Algorithm Based on Node Cluster. In: Wu, W., Du, H. (eds) Algorithmic Aspects in Information and Management. AAIM 2021. Lecture Notes in Computer Science(), vol 13153. Springer, Cham. https://doi.org/10.1007/978-3-030-93176-6_25
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DOI: https://doi.org/10.1007/978-3-030-93176-6_25
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