德曼特:DropMessage高图注意力网络信息传播预测预测
Qi Ouyang1, Hongchang Chen2, Shuxin Liu2
1People's Liberation Army Strategic Support Force Information Engineering University, Zhengzhou, China.
Big data
|October 23, 2024
概括
本研究介绍了DropMessage超图注意力网络,用于预测社交网络中的信息级联. 该模型通过考虑全球用户依赖,并采用一种新的立即下降方法来提高预测的准确性和稳定性.
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
- 机器学习用于网络科学网络科学
背景情况:
- 预测信息级联对于理解社交网络动态至关重要.
- 现有的模型往往忽视了全球用户的依赖性,缺乏稳定性.
- 描述动态用户交互偏好仍然是一个挑战.
研究的目的:
- 为预测信息传播级联提出一个强大而准确的模型.
- 解决有关全球依赖和模型稳定性的现有方法的局限性.
- 增强在社交网络中的动态用户交互偏好的表征.
主要方法:
- 从级联序列构建一个超图,以捕捉全球依赖.
- 开发超图注意力网络,使用时间标记的子图来学习用户交互.
- 实施一个封闭的融合策略和一种新的DropMessage方法,以提高稳定性.
主要成果:
- 拟议的DropMessage高图注意力网络在MAP@k和Hits@K指标上显著优于最先进的模型.
- 该模型在数据扰动下,与现有方法相比,显示出更高的预测性能.
- 在三个真实世界数据集上的实验验证证证了该模型的有效性.
结论:
- DropMessage高图注意力网络在信息级联预测方面取得了重大进展.
- 该模型有效地捕捉了全球依赖关系,并通过新的技术提高了稳定性.
- 这种方法为分析社交网络中信息传播提供了更准确,更可靠的方法.
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