Open Access
ARTICLE
A PageRank-Based WeChat User Impact Assessment Algorithm
Qiong Wang1, Yuewen Luo1, Hongliang Guo1, Peng Guo2, Jinghao Wei1, Tie Lin1,*
1 Hunan University of Finance and Economics, Changsha, China
2 University Malaysia Sabah, Kota Kinabalu, Malaysia
* Corresponding Author: Tie Lin. Email:
Journal of New Media 2021, 3(2), 53-62. https://doi.org/10.32604/jnm.2021.018762
Received 20 March 2021; Accepted 30 March 2021; Issue published 23 April 2021
Abstract
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.
Keywords
Cite This Article
Q. Wang, Y. Luo, H. Guo, P. Guo, J. Wei
et al., "A pagerank-based wechat user impact assessment algorithm,"
Journal of New Media, vol. 3, no.2, pp. 53–62, 2021.