Jiaming Wang2, Xiaolan Xie1,2,*, Xiaochun Cheng3, Yuhan Wang2
Computer Systems Science and Engineering, Vol.43, No.3, pp. 997-1008, 2022, DOI:10.32604/csse.2022.027005
- 09 May 2022
Abstract
There is a large amount of information in the network data that we can exploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem. Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning. Therefore, we propose an improved density peak clustering algorithm (ILDPC) for the community detection problem, which improves the local density mechanism in the original algorithm and can better accommodate class clusters of different
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