Open Access

ARTICLE

Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

Jiaming Wang², Xiaolan Xie^1,2,*, Xiaochun Cheng³, Yuhan Wang²

1 Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin, 541006, Guangxi, China
2 School of Information Science and Engineering, Guilin University of Technology, Guilin, 541006, Guangxi, China
3 Department of Computer Science, Middlesex University, London

* Corresponding Author: Xiaolan Xie. Email:

Computer Systems Science and Engineering 2022, 43(3), 997-1008. https://doi.org/10.32604/csse.2022.027005

Received 08 January 2022; Accepted 23 March 2022; Issue published 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 shapes. And we study the community detection in network data. The algorithm is paired with the benchmark model Graph sample and aggregate (GraphSAGE) to show the adaptability of ILDPC for community detection. The plotted decision diagram shows that the ILDPC algorithm is more discriminative in selecting density peak points compared to the original algorithm. Finally, the performance of K-means and other clustering algorithms on this benchmark model is compared, and the algorithm is proved to be more suitable for community detection in sparse networks with the benchmark model on the evaluation criterion F1-score. The sensitivity of the parameters of the ILDPC algorithm to the low-dimensional vector set output by the benchmark model GraphSAGE is also analyzed.

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Keywords

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