摘要
现有在线内容流行度预测方法忽略对传播级联演化过程中的结构和时序特征的捕获.针对此问题,文中提出基于图注意力时空神经网络的在线内容流行度预测模型.利用图注意力机制学习在线内容的级联结构表示,利用时序卷积网络捕获传播级联的时序特征,通过全卷积层映射在线内容的流行度.在新浪微博和美国物理学会引文两个不同场景的数据集上的实验表明,文中方法的流行度预测性能较优.
The existing methods for predicting the popularity of online contents ignore the structural and temporal characteristics in the dynamic process of information cascades.To address this problem,a graph attention based spatial-temporal neural network(GAST-Net)is proposed to predict the popularity of online contents.The graph attention mechanism is adopted to learn the representation of cascade structure of online contents.Then,a temporal convolutional network is employed to capture the temporal features of information cascade.Finally,the popularity of online contents is mapped through a fully convolutional layer.Experimental results on datasets of Sina Weibo and American Physical Society demonstrate that GAST-Net model consistently outperforms the state-of-the-art methods.
作者
鲍鹏
徐昊
BAO Peng;XU Hao(School of Software Engineering,Beijing Jiaotong University,Beijing 100044)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2019年第11期1014-1021,共8页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61702031)
北京市优秀人才培养青年骨干个人项目(No.2017000020124G054)
中央高校基本科研业务费项目(No.2018JBM072)
中国科学院网络数据科学与技术重点实验室开放课题项目(No.CASNDST201702)~~
关键词
流行度预测
信息传播
图注意力网络
时序卷积网络
Popularity Prediction
Information Diffusion
Graph Attention Network
Temporal Convolutional Network