LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions
To address the problem of maximizing desired opinions in social networks, we present the Limited Opinion Maximization with Dynamic Propagation Optimization framework, which is grounded in information entropy theory. Innovatively, we introduce the concept of node expression capacity, which quantifies...
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MDPI AG
2025-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/4/360 |
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| author | Xuan Wang Bin Wu Tong Wu |
| author_facet | Xuan Wang Bin Wu Tong Wu |
| author_sort | Xuan Wang |
| collection | DOAJ |
| description | To address the problem of maximizing desired opinions in social networks, we present the Limited Opinion Maximization with Dynamic Propagation Optimization framework, which is grounded in information entropy theory. Innovatively, we introduce the concept of node expression capacity, which quantifies the uncertainty of users’ expression intentions via entropy and effectively identifies the impact of silent nodes on the propagation process. Based on this, in terms of seed node selection, we develop the Limited Opinion Maximization algorithm for multi-stage seed selection, which dynamically optimizes the seed distribution among communities through a multi-stage seeding approach. In terms of node opinion changes, we establish the LODP dynamic opinion propagation model, reconstructing the node opinion update mechanism and explicitly modeling the entropy-increasing effect of silent nodes on the information propagation path. The experimental results on four datasets show that LOMDP outperforms six baseline algorithms. Our research effectively resolves the problem of maximizing desired opinions and offers insights into the dynamics of information propagation in social networks from the perspective of entropy and information theory. |
| format | Article |
| id | doaj-art-c01416ff12e341bbb0d1774a022621cd |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-c01416ff12e341bbb0d1774a022621cd2025-08-20T02:28:34ZengMDPI AGEntropy1099-43002025-03-0127436010.3390/e27040360LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression IntentionsXuan Wang0Bin Wu1Tong Wu2School of Cyberspace Security, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100876, ChinaTo address the problem of maximizing desired opinions in social networks, we present the Limited Opinion Maximization with Dynamic Propagation Optimization framework, which is grounded in information entropy theory. Innovatively, we introduce the concept of node expression capacity, which quantifies the uncertainty of users’ expression intentions via entropy and effectively identifies the impact of silent nodes on the propagation process. Based on this, in terms of seed node selection, we develop the Limited Opinion Maximization algorithm for multi-stage seed selection, which dynamically optimizes the seed distribution among communities through a multi-stage seeding approach. In terms of node opinion changes, we establish the LODP dynamic opinion propagation model, reconstructing the node opinion update mechanism and explicitly modeling the entropy-increasing effect of silent nodes on the information propagation path. The experimental results on four datasets show that LOMDP outperforms six baseline algorithms. Our research effectively resolves the problem of maximizing desired opinions and offers insights into the dynamics of information propagation in social networks from the perspective of entropy and information theory.https://www.mdpi.com/1099-4300/27/4/360positive opinion maximizationopinion dynamicsentropyinformation theorydesired opinion maximization |
| spellingShingle | Xuan Wang Bin Wu Tong Wu LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions Entropy positive opinion maximization opinion dynamics entropy information theory desired opinion maximization |
| title | LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions |
| title_full | LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions |
| title_fullStr | LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions |
| title_full_unstemmed | LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions |
| title_short | LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions |
| title_sort | lomdp maximizing desired opinions in social networks by considering user expression intentions |
| topic | positive opinion maximization opinion dynamics entropy information theory desired opinion maximization |
| url | https://www.mdpi.com/1099-4300/27/4/360 |
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