Abstract：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.
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2019, Vol. 32 
