基于图注意力时空神经网络的在线内容流行度预测

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.

Predicting Popularity of Online Contents via Graph Attention Based Spatial-Temporal Neural Networ

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.