使用有向图建模社交网络、利用深度学习方法计算结算图中节点的影响力是社交研究领域的重要分支。为解决图神经网络不能很好地依据构建的节点特征计算节点影响力的问题，受GraphGPS启发，设计一个热度模块，提出一种融合真实网络中语义、行为、热度信息的节点影响力计算方法。首先依据节点多种中心性和正太分布采样得到的自信息作为节点的初始语义特征；其次，通过图神经网络融合节点特征；再次，通过热度模块学习节点热度信息；最后，融合提取的语义、行为、热度特征，实现节点影响力计算。在4个真实网络数据集上进行实验，结果表明添加热度模块的模型能有效的计算节点影响力。

Modeling social networks using directed graphs and calculating the influence of nodes in settlement graphs using deep learning methods are important branches in the field of social research. In order to solve the problem that graph neural networks cannot well calculate the influence of nodes based on constructed node features, inspired by GraphGPS, a heat module was designed to propose a method of calculating the influence of nodes that integrated semantic, behavioral, and heat information in real networks. Firstly, the self-information obtained based on nodes' multiple centrality and orthogonal distribution sampling was used as the initial semantic features of the nodes. Secondly, the node features were fused by graph neural network. Once again, the node heat information was learned by the heat module. Finally, the fusion of the extracted semantic, behavioral, and heat features was implemented to calculate the node influence. Experiments were conducted on four real network datasets. The results show that the model with the addition of the heat module can effectively calculate node influence.