Application of percolation model in spreading dynamics driven by social networks big data

¹ School of Systems Science and Engineering, Sun Yat-sen University - Guangzhou Guangdong 510006, PRC
² School of Computer Science and Engineering, Sun Yat-sen University - Guangzhou Guangdong 510006, PRC
³ Department of Statistics and Data Science, College of Science, Southern University of Science and Technology Shenzhen Guangdong 518055, PRC
⁴ School of Mathematics, Sun Yat-sen University - Guangzhou Guangdong 510275, PRC
⁵ Department of Physics, Jinan University - Guangzhou Guangdong 510632, PRC

^(a) E-mail: xiyunzhang@jnu.edu.cn (corresponding author)
^(b) E-mail: huyq@sustech.edu.cn (corresponding author)

Received: 5 January 2023
Accepted: 28 February 2023

Abstract

Spreading dynamics is a common yet sophisticated phenomenon in real life, and percolation theory is widely applied in analysis of this dynamics due to its conciseness and efficiency. With the development of information technology, the quality and quantity of available data are being improved. Although this offers a chance to describe and understand empirical spreading phenomena more comprehensively and accurately, complicated dynamics brought by massive data pose new challenges to the study of social contagion based on percolation theory. In this prospective, we show, by analyzing examples, how the percolation theory is used to describe the information transmission on social networks driven by big data. We also explore the indirect influence mechanism behind the spread of scientific research behavior, and develop a new algorithm to quantify the global influence of nodes from the local topology. Finally, we propose, based on these example studies, several possible new directions of percolation theory in the study of social contagion driven by big data.