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Opinion Maximization Based on Fairness in Social Networks
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Yingying Zhai1, Zhenling Han1, Zefang Dong1, Xiaochun Yang2, Bin Wang1, 2
Data Science and Engineering | 2026, 11(1) : 84 - 99
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Data Science and Engineering | 2026, 11(1): 84-99
RESEARCH PAPERS
Opinion Maximization Based on Fairness in Social Networks
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Yingying Zhai1, Zhenling Han1, Zefang Dong1, Xiaochun Yang2, Bin Wang1, 2
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Published: 2026-03-01 doi: 10.1007/s41019-025-00307-9
Outline
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Opinion maximization has attracted much attention in viral marketing. It selects an initial seed user set to disseminate user opinions on the target product and finally produces more positive opinions in social networks. In earlier studies, a critical but not studied problem is the fairness of information dissemination in groups with sensitive characteristic (such as age or race). People prefer to promote products for target users (majority groups) rather than in sensitive characteristic groups (minority groups). That leads to the differences in information dissemination between minority groups. In addition, social networks have the in-depth structural information. Therefore, in this paper, we design opinion maximization based on fairness framework (OMBF) using graph attention networks (GAT) to exploit more network information, and only consider the fairness in minority groups. OMBF composes of three parts: (1) the determination of candidate nodes according to node representations, (2) the dynamic changes in opinions, (3) the selection of final seed nodes. Firstly, we utilize GAT to obtain node representations and to determinate candidate nodes and design a node opinion formation model to model the dynamic changes in opinions. Then, we use the fair constraint value to ensure the fairness in the information dissemination process of minority groups. Based on above, final seed nodes are selected. We conduct experiments on synthetic and real-world datasets to show the effectiveness of our approach. The results indicate that the total opinions of active nodes in all nodes and fair values in minority groups are better than the chosen state-of-the-art benchmarks.

Social networks  /  Opinion maximization  /  Fairness  /  Node representations  /  Sensitive characteristic
Yingying Zhai, Zhenling Han, Zefang Dong, Xiaochun Yang, Bin Wang. Opinion Maximization Based on Fairness in Social Networks[J]. Data Science and Engineering, 2026 , 11 (1) : 84 -99 . DOI: 10.1007/s41019-025-00307-9
  • National Key Research and Development Program of China(2024YFF0617700)
  • National Natural Science Foundation of China(U22A2025)
  • Fundamental Research Funds for the Central Universities(N2216011)
Year 2026 volume 11 Issue 1
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Article Info
doi: 10.1007/s41019-025-00307-9
  • Receive Date:2025-01-22
  • Online Date:2026-08-06
  • Published:2026-03-01
Article Data
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History
  • Received:2025-01-22
  • Revised:2025-04-05
  • Accepted:2025-06-25
Funding
National Key Research and Development Program of China(2024YFF0617700)
National Natural Science Foundation of China(U22A2025)
Fundamental Research Funds for the Central Universities(N2216011)
Affiliations
    1College of Computer Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
    2School of Mathematics and Statistics, Liaoning University, Shenyang, China

Corresponding:

Yingying Zhai 
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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