Data mining in social networks brings an indispensable role for the construction of smart cities from the perspective of social development. Link prediction is an important task of data mining, especially in the knowledge graph, which is also called knowledge graph completion. Link prediction aims to find missing links or predict potential links according to the current social network. The most existing link prediction methods focus on static information in social networks, such as topology and node attributes, which are partly provided by users. When users are unwilling to provide or intentionally hide these static features, traditional link prediction methods cannot achieve ideal performance. The dynamic information of social influence propagation in social networks can avoid the user's subjective impact and better reflect the relationship between users. In addition, users show different degrees of interest and authority on various topics in the real world, leading to different influence propagation patterns. Therefore, we use context of social influence to optimize the topic-aware influence propagation model to improve the performance of link prediction. In this paper, we propose a new multi-output graph neural network framework to capture influence propagation in social networks and model the influence of users in different roles. In this way, the underlying information of influence between users can be used to construct new features to improve the performance of link prediction. Our experiments conduct the method on multiple benchmark datasets. The experimental results show that the modeling of context is effective, and our model outperforms the compared state-of-the-art link prediction methods.

从社会发展的角度来看，社交网络中的数据挖掘为智慧城市的建设带来了不可或缺的作用。链接预测是数据挖掘的重要任务，尤其是在知识图中，这也称为知识图完成。链接预测旨在根据当前的社交网络查找丢失的链接或预测潜在的链接。最现有的链接预测方法集中于社交网络中的静态信息，例如拓扑和节点属性，这些信息部分由用户提供。当用户不愿提供或故意隐藏这些静态功能时，传统的链接预测方法将无法达到理想的性能。社交网络中社交影响力传播的动态信息可以避免用户的 的主观影响，并更好地反映用户之间的关系。此外，用户对现实世界中各个主题的兴趣和权威程度也不尽相同，从而导致影响传播方式也有所不同。因此，我们使用社会影响的上下文来优化主题感知的影响传播模型，以提高链接预测的性能。在本文中，我们提出了一种新的多输出图神经网络框架，以捕获社交网络中的影响传播并为不同角色的用户影响建模。这样，用户之间影响的基础信息可用于构建新功能，以提高链接预测的性能。我们的实验在多个基准数据集上实施了该方法。实验结果表明，上下文建模是有效的，