Abstract:

Aiming at the problem that in the dynamic evolution of information diffusion， it is difficult to effectively capture structural features， temporal features， and the interactive expression between them， an information diffusion prediction model based on Transformer and Relational Graph Convolutional Network （TRGCN） was proposed. Firstly， a dynamic heterogeneous graph composed of the social network graph and the diffusion cascade graph was constructed. The structural features of each node in this graph were then extracted using Relational Graph Convolutional Network （RGCN）. Secondly， the time embedding of each node was re-encoded using Bi-directional Long Short-Term Memory （Bi-LSTM） network. Then a time decay term was introduced to give different weights to the nodes at different time positions， so as to obtain the temporal features of nodes. Finally， structural features and temporal features were input into Transformer and then merged. Finally， the spatial-temporal features were obtained for information diffusion prediction. The experimental results on three real datasets of Twitter， Douban and Memetracker show that compared with the optimal model in the comparison experiment， the Hits@100 of TRGCN increase by 3.18%， 5.96% and 3.34% respectively， the Map@100 of TRGCN increase by 11.60%， 19.72% and 8.47% respectively， proving its validity and rationality.

Key words: information diffusion prediction, Transformer, Relational Graph Convolutional Network (RGCN), Bi-directional Long Short-Term Memory (Bi-LSTM) network, spatial-temporal feature

摘要：

针对在信息传播动态演化中，结构特征和时序特征以及两者间的交互表达难以有效捕获的问题，提出一种基于Transformer和关系图卷积网络的信息传播预测模型（TRGCN）。首先，构建由社交关系图和传播级联图组合而成的异构图，使用关系图卷积网络（RGCN）提取图中各节点的结构特征；其次，使用双向长短期记忆（Bi-LSTM）网络对各节点的时间嵌入重新编码，引入时间衰减项以不同的权重赋予不同时间位置的节点，获得节点的时序特征；最后，将结构特征和时序特征输入Transformer进行融合，得到时空特征以预测信息传播。在Twitter、Douban和Memetracker这3个真实数据集上的实验结果表明，相较于对比实验中的最优模型，TRGCN的Hits@100指标分别提升3.18%，5.96%和3.34%，Map@100指标分别提升11.60%，19.72%和8.47%，验证了所提模型的有效性和合理性。

关键词: 信息传播预测, Transformer, 关系图卷积网络, 双向长短期记忆网络, 时空特征