基于时空注意力的社交网络信息级联预测模型

Spatio-temporal Attention Based Information Cascade Prediction for Social Network

针对目前信息级联预测模型的构建多基于级联的时序信息或者空间拓扑结构、极少考虑两者的结合问题,该文提出一种面向社交网络的基于深度学习方法的信息级联预测(Information Cascade Prediction,ICP)模型。首先,使用拉普拉斯矩阵对级联节点采样,生成空间序列;然后,通过结合了图卷积网络的双向循环神经网络学习节点的时序信息和空间结构信息;最后,通过注意力机制对信息级联的时序信息和空间信息进行联合建模并在真实数据集上进行实验。实验结果表明:与现有研究相比该文提出的ICP模型具有较高的预测精度,预测精度损失降低约为1%~8%,表明ICP模型是合理、有效的。

Existing information cascade prediction models are established by cascaded time series information or spatial topology.A social network-oriented information cascade prediction(Information Cascade Prediction,ICP)model is proposed to jointly model these two information based on deep learning.First,the Laplacian matrix is used to sample the cascaded nodes to generate a spatial sequence.Then the timing information and spatial structure information of the nodes are learned through the Bi-LSTM plus the graph convolutional network.And the information is finally cascaded through the attention mechanism.Experimental results show that the proposed model has higher prediction accuracy compared with existing method,reducing prediction error by about 1%to 8%.