@Article{jnm.2021.018762,
AUTHOR = {Qiong Wang, Yuewen Luo, Hongliang Guo, Peng Guo, Jinghao Wei, Tie Lin},
TITLE = {A PageRank-Based WeChat User Impact Assessment Algorithm},
JOURNAL = {Journal of New Media},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {53--62},
URL = {http://www.techscience.com/JNM/v3n2/42389},
ISSN = {2579-0129},
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.},
DOI = {10.32604/jnm.2021.018762}
}