在基于深度强化学习的社交网络中实现平衡影响力最大化
Shuxin Yang1, Quanming Du1, Guixiang Zhu2
1School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China.
概括
本研究引入了一种新的框架,用于在社交网络中实现平衡影响力最大化,通过结合实体相关性和减少计算需求来解决现有方法的局限性,以获得更好的现实应用.
科学领域:
- 社交网络分析 社交网络分析
- 信息传播的动态信息传播的动态
- 计算社会科学 计算社会科学
背景情况:
- 平衡的影响力最大化对于缓解社交网络中的波泡和回声室至关重要.
- 现有的方法忽视实体相关性,需要广泛的扩散采样,限制可扩展性.
研究的目的:
- 为平衡影响力最大化提出一个新的框架,以考虑实体相关性并提高效率.
- 在大型社交网络中提高平衡影响力最大化的准确性和可扩展性.
主要方法:
- 开发了一个基于深度强化学习 (BIM-DRL) 的平衡影响力最大化框架.
- 引入使用历史用户行为序列的实体相关性评估模块.
- 设计了一个基于深度强化学习的种子节点选择模块,以优化平衡的影响.
主要成果:
- 拟议的BIM-DRL框架有效评估实体相关性对信息传播的影响.
- 与最先进的方法相比,BIM-DRL显著提高了平衡影响传播和平衡传播的准确性.
- 该框架在六个现实网络数据集中展示了卓越的性能.
结论:
- BIM-DRL提供了一个更准确,更可扩展的解决方案,用于在社交网络中实现平衡的影响力最大化.
- 实体相关性和深度强化学习的结合推动了影响力最大化领域的发展.
- 这种方法提供了一种强有力的方法来促进多样化的信息曝光,并防止回声室.
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