Research on the Maximization of Influence in Social Network Information Dissemination under Topic Preference

Publication Date

1-1-2021

Document Type

Conference Proceeding

Publication Title

Proceedings - IEEE 7th International Conference on Big Data Computing Service and Applications, BigDataService 2021

DOI

10.1109/BigDataService52369.2021.00029

First Page

184

Last Page

189

Abstract

With the rapid development of Internet technology, more and more people begin to pay attention to and study the information mining and data analysis in social networks, and the maximization of influence widely used in marketing have gradually become the focus of people's research. Nowadays, most of the researches on the maximization of influence under topic preference in social network information dissemination only tend to judge the influence of nodes on a certain information from the interaction of nodes to the topic content of information. The combination of the topology of social networks and the topic content of information dissemination is not considered. In view of this situation, an influence maximization algorithm under topic preference(IMATP algorithm) is proposed, which evaluates the comprehensive influence of nodes by combining node centrality and theme influence. Experimental results on data sets of different sizes from Twitter show that the algorithm proposed in this paper is closer to the actual propagation situation. and compared with the classical algorithm, this algorithm not only improves the scope of influence, but also improves the running efficiency.

Funding Number

2018YFC0407106

Funding Sponsor

National Key Research and Development Program of China

Keywords

Impact maximization, Information dissemination, Node centrality, Social network, Subject authority

Department

Applied Data Science; Computer Engineering

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