基于时空Transformer的社交网络信息传播预测

Social Network Information Diffusion Prediction Based on Spatial-Temporal Transformer

随着社交网络的日益普及和广泛应用,信息传播预测逐渐成为了社交网络分析领域的一个热点研究问题.之前大部分研究要么只利用信息传播序列,要么只利用用户之间的社交网络来进行预测,难以对信息传播过程的复杂性进行有效建模.此外,常用于信息传播预测的循环神经网络(recurrent neural network,RNN)及其变体难以有效捕获信息之间的相关性.为解决上述问题,提出了一个新的基于时空Transformer的社交网络信息传播预测模型STT.该模型首先构建由社交网络图和动态传播图组成的异构图并使用图卷积网络(graph convolutional network,GCN)来学习用户的结构特征;然后将用户的时序特征和结构特征放入到Transformer中进行融合来获取时空特征;为有效融合用户的时序特征和结构特征,提出了一种新的残差融合方式来替代Transformer中原有的残差连接;最后利用Transformer来进行信息传播预测.真实数据集上的大量实验验证了模型STT的有效性.

With the increasing popularity and wide application of social networks,information diffusion prediction has gradually become a hot research topic in the field of social network analysis.Most previous studies either only use the information diffusion sequence or only use the social network between users to make prediction,failing to effectively model the complexity of the information diffusion process.In addition,recurrent neural network(RNN)and its variants,which are commonly used in information diffusion prediction,are difficult to capture the correlation between information effectively.To address the above problems,we propose a novel social network information diffusion prediction model called STT based on spatial-temporal Transformer.First,we construct a heterogeneous graph composed of a social network graph and a dynamic diffusion graph,and use graph convolutional network(GCN)to learn the users structural features.Then,the users temporal features and structural features are put into the Transformer for fusion to obtain users spatial-temporal features.In order to effectively fuse the users temporal features and structural features,a novel residual fusion method is proposed to replace the original residual connection in Transformer.Finally,the Transformer is used for information diffusion prediction.Extensive experiments on real datasets demonstrate the effectiveness of our proposed model STT.