Research and Analysis of Influence Maximization Techniques in Online Network Communities Based on Social Big Data

Many online network communities, such as Facebook, Twitter, Tik Tok, Weibo, etc., have developed rapidly and become the bridge connecting the physical social world and virtual cyberspace. Online network communities store a large number of social relationships and interactions between users. How to analyze diffusion of influence from this massive social data has become a research hotspot in the applications of big data mining in online network communities. A core issue in the study of influence diffusion is influence maximization. Influence maximization refers to selecting a few nodes in a social network as seeds, so as to maximize influence spread of seed nodes under a specific diffusion model. Focusing on two core aspects of influence maximization (i.e., models and algorithms), this paper summarizes the main achievements of research on influence maximization in the computer field in recent years. Finally, this paper briefly discusses issues, challenges, and future research directions in the research and application of influence maximization.

Automated summary

The paper "Research and Application of Influence Maximization in Online Network Communities" summarizes the recent achievements in the study of influence maximization in the computer field, with a focus on models and algorithms. It also discusses the issues, challenges, and future research directions in this field.