A PageRank-Based WeChat User Impact Assessment Algorithm

• Published in: Journal of new media Vol. 3; no. 2; pp. 53 - 62
• Main Authors: Wang, Qiong, Luo, Yuewen, Guo, Hongliang, Guo, Peng, Wei, Jinghao, Lin, Tie
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2021
• Subjects: Algorithms; Evaluation; Graph theory; Mobile commerce; Search algorithms; Social networks; Sociology; User behavior; User groups
• ISSN: 2579-0110; 2579-0129
• DOI: 10.32604/jnm.2021.018762

In recent years, the mobile Internet has developed rapidly, and the network social platform has emerged as the times require, and more people make friends, chat and share dynamics through the network social platform. The network social platform is the virtual embodiment of the social network, each user represents a node in the directed graph of the social network. As the most popular online social platform in China, WeChat has developed rapidly in recent years. Large user groups, powerful mobile payment capabilities, and massive amounts of data have brought great influence to it. At present, the research on WeChat network at home and abroad mainly focuses on communication and sociology, but the research from the angle of influence is scarce. Therefore, based on the basic principle of PageRank, this paper proposes an influence evaluation model WURank algorithm suitable for WeChat network users. This algorithm takes into account the shortcomings of the traditional PageRank algorithm, and objectively evaluates the real-time influence of WeChat users from the perspective of WeChat user behavior (including: sharing, commenting, mentioning, collecting, likes) and time factors.