CST-Net:社区引导的结构时间卷积网络,用于普及预测
Xuxu Zheng1,2, Peng Bao3, Lin Qi3
1University of Chinese Academy of Sciences, Beijing, China.
PeerJ. Computer science
|September 24, 2025
概括
预测在线内容的受欢迎程度至关重要. 一个新的深度学习框架,CST-Net,通过分析用户社区和信息布,有效地预测内容的受欢迎程度,优于现有的方法.
科学领域:
- 计算社会科学 计算社会科学
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 预测在线内容的受欢迎程度在各个领域至关重要.
- 挑战包括人气不平等和复杂的影响因素.
- 现有的方法 (特征驱动,生成,深度学习) 有局限性.
研究的目的:
- 引入CST-Net,这是一个端到端的深度学习框架,用于改进人气预测.
- 为了解决当前流行度预测方法的缺陷.
主要方法:
- 从历史互动中学习低维用户嵌入.
- 将用户聚合到社区中,并将信息布表示为社区交互矩阵.
- 应用了卷积架构来提取级联表示.
- 结合结构和时间特征,用于增量流行预测.
主要成果:
- 在微博和学术引用数据集上,CST-Net表现出卓越的性能.
- 该模型始终优于现有的竞争性人气预测方法.
- 对人口规模数据集的验证证实了有效性.
结论:
- CST-Net提供了一种强大而有效的方法来预测在线内容的受欢迎程度.
- 该框架能够捕捉复杂的级联动态是其成功的关键.
- 这项工作推动了计算社会科学和预测建模领域的发展.
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