基于级联时空特征的信息传播预测方法

现有信息传播预测方法对级联序列和拓扑结构独立建模,难以学习级联时序特征和结构特征在嵌入空间的交互表达,造成对信息传播动态演化的刻画不足.因此,文中提出基于级联时空特征的信息传播预测方法.基于社交关系网络和传播路径构建异质图,使用图神经网络学习异质图和社交关系网络节点的结构上下文,引入门控循环单元提取级联时序特征,融合结构上下文和时序特征,构建级联时空特征,进行信息传播的微观预测.在Twitter、Memes数据集上的实验表明,文中方法性能得到一定提升.

Information Diffusion Prediction Based on Cascade Spatial-Temporal Feature

The existing information diffusion prediction methods model the cascade sequences and topological structure independently. And thus it is difficult to learn the interactive expression of cascade temporal and structural features in the embedded space, and the portrayal of dynamic evolution of information diffusion is insufficient. Aiming at this problem, an information diffusion prediction method based on cascade spatial-temporal feature is proposed. Based on the social network and diffusion paths, the heterogeneous graphs are constructed. The structural context of nodes of heterogeneous graphs and social network is learned by graph neural network, while the cascade temporal feature is captured by gated recurrent unit. To make microscopic information prediction, the cascade spatial-temporal feature is constructed by fusing structure context and temporal feature. The experimental results on Twitter and Memes datasets demonstrate that the performance of the proposed method is improved to a certain extent.