Paper Info
Title
An Influence Maximization Algorithm for Dynamic Social Networks Based on Effective Links
Abstract
The connection between users in social networks can be maintained for a certain period of time, and the static network structure formed provides the basic conditions for various kinds of research, especially for discovering customer groups that can generate great influence, which is important for product promotion, epidemic prevention and control, and public opinion supervision, etc. However, the computational process of influence maximization ignores the timeliness of interaction behaviors among users, the screened target users cannot diffuse information well, and the time complexity of relying on greedy rules to handle the influence maximization problem is high. Therefore, this paper analyzes the influence of the interaction between nodes in dynamic social networks on information dissemination, extends the classical independent cascade model to a dynamic social network dissemination model based on effective links, and proposes a two-stage influence maximization solution algorithm (Outdegree Effective Link-OEL) based on node degree and effective links to enhance the efficiency of problem solving. In order to verify the effectiveness of the algorithm, five typical influence maximization methods are compared and analyzed on four real data sets. The results show that the OEL algorithm has good performance in propagation range and running time.
Year
DOI
Venue
2022
10.3390/e24070904
ENTROPY
Keywords
DocType
Volume
influence maximization, dynamic social networks, effective link, independent cascade model
Journal
24
Issue
ISSN
Citations 
7
1099-4300
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Baojun Fu100.68
Zhang Jianpei28321.93
Hongna Bai300.34
Yuting Yang44410.79
Yu He501.01