增强图表共进化网络用于使用同化修改情感算法进行社交网络分析
Hsiao-Hui Li1, Po-Chun Chang2, Yuan-Hsun Liao3
1Department of Maritime Information and Technology, National Kaohsiung University of Science and Technology, Cijin Campus, Gaoxiong, 805301, Taiwan.
Scientific reports
|March 2, 2026
概括
新的同化修改情绪 (AME) 算法通过模拟情绪过渡来改进社交网络分析. 这种人工智能方法比传统方法提高了情绪特征提取准确度.
科学领域:
- 人工智能的人工智能
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
背景情况:
- 传统的标签传播算法 (LPA) 缺乏情感表现,并且受到局部依赖的限制.
- 现有的方法,如异步标签传播和卢温算法,不能有效地捕捉动态情绪状态.
研究的目的:
- 引入同化修改情绪 (AME) 算法,用于社交网络分析的LPA增强.
- 为了解决情绪特征提取和动态情绪状态模拟的局限性.
- 提高AI模型在社交网络环境中的准确性和性能.
主要方法:
- AME算法集成了光谱算法,马尔科夫链,图形粗化和链接预测.
- 它模拟和优化社交网络中的情绪过渡.
- 多标签编码用于增强动态情绪状态的标签表示.
主要成果:
- 与传统的LPA方法相比,AME算法显示出更高的性能.
- 在准确度和损失值方面都观察到改善.
- 改进的方法有效模拟动态的情绪状态.
结论:
- 在社交网络分析和情感特征提取方面,AME算法为AI提供了显著的进步.
- 它提供了一种更强大的方法来理解和建模网络中的情感动态.
- 这些发现表明,在人工智能驱动的社交媒体分析中,实际应用的潜力很大.
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