Community Evolution Prediction Based on Multivariate Feature Sets and Potential Structural Features

Jing Chen, Haitong Zhao, Xinyu Yang (), Mingxin Liu (), Zeren Yu and Miaomiao Liu
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Jing Chen: College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Haitong Zhao: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
Xinyu Yang: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
Mingxin Liu: College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Zeren Yu: International Hotel Management, City University of Macau, Macau 999078, China
Miaomiao Liu: College of Computer and Information Technology, Northeast Petroleum University, Qinhuangdao 066004, China

Mathematics, 2022, vol. 10, issue 20, 1-16

Abstract: The current study on community evolution prediction ignores the problem of internal community topology characteristics and does not take feature sets extraction into account. Therefore, the MF-PSF (Multivariate Feature sets and Potential Structural Features) method based on multivariate feature sets and potential structural features for community evolution prediction is proposed in this paper. Firstly, the multivariate feature sets are built from four aspects: community core node features, community structural features, community sequential features and community behavior features. Secondly, the community’s potential structural characteristics based on DeepWalk and spectral propagation theories are extracted, and the overall community’s internal structural characteristics and vertex distribution are analyzed. Finally, the community’s multivariate structural features and potential structural features are merged to predict community evolution events, and the importance of each feature in the process of evolutionary prediction is discussed. The experimental results show that compared with other community evolution prediction methods, the MF-PSF prediction method not only provides a foundation for analyzing the influence of various feature sets on predicted events, but it also effectively improves the accuracy of evolution prediction.

Keywords: social networks; community evolution prediction; network representation learning; multivariate feature sets; structural features (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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