基于图注意力时空神经网络的在线内容流行度预测

doi:10.16451/j.cnki.issn1003-6059.201911006

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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.

Key words： Popularity Prediction Information Diffusion Graph Attention Network Temporal Convolutional Network

Received: 18 August 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61702031), Beijing Excellent Talents Supporting Program(No.2017000020124G054), Fundamental Research Funds for the Central Universities(No. 2018JBM072), and CAS Key Laboratory of Network Data Science and Technology(No. CASNDST201702)

Corresponding Authors: BAO Peng, Ph.D., associate professor. His research interests include data mining, social computing and machine learning.

About author:: XU Hao, master student. His research interests include data mining and machine lear-ning.

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Cite this article:

BAO Peng,XU Hao. Predicting Popularity of Online Contents via Graph Attention Based Spatial-Temporal Neural Networ[J]. , 2019, 32(11): 1014-1021.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201911006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I11/1014

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