LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new framework for maximizing opinions in social networks using information entropy. It enhances opinion spread by considering silent users and optimizing seed selection for better information propagation.
Area Of Science
- Social Network Analysis
- Information Theory
- Computational Social Science
Background
- Maximizing desired opinions in social networks is a complex challenge.
- Existing models often overlook the influence of silent users on information propagation.
- Information entropy offers a novel perspective for understanding user expression and influence.
Purpose Of The Study
- To develop a framework for maximizing desired opinions in social networks.
- To introduce and quantify the concept of node expression capacity using entropy.
- To model the impact of silent nodes and optimize opinion propagation dynamics.
Main Methods
- Introduced the Limited Opinion Maximization with Dynamic Propagation Optimization (LOMDP) framework.
- Developed a multi-stage seed selection algorithm optimizing seed distribution.
- Established the LODP dynamic opinion propagation model incorporating entropy.
Main Results
- The LOMDP framework effectively quantifies user expression uncertainty via node expression capacity.
- The LODP model explicitly incorporates the entropy-increasing effect of silent nodes.
- Experimental results demonstrate LOMDP outperforms six baseline algorithms across four datasets.
Conclusions
- The LOMDP framework provides an effective solution for maximizing desired opinions in social networks.
- Node expression capacity and the impact of silent nodes are crucial for propagation dynamics.
- This research offers new insights into social network information propagation through entropy and information theory.
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