The potential impact of social influence prediction has become a hot topic in the current graph data mining area. This paper proposes a deep learning framework named Multi-view Influence prediction network (MvInf) which combines multi-view learning and graph attention neural network together to address the problem of social influence prediction. MvInf takes different attribute features of users as the input of graph attention network and uses the complementarity and consistency between different views to enhance learning performance and thus to better predict user behavior. Experiments performed on four standard datasets (Open Academic Graph, Twitter, Weibo, and Digg) demonstrate that the proposed MvInf model can obtain better performance than previous single view-based approach.

社会影响力预测的潜在影响已成为当前图形数据挖掘领域的热门话题。本文提出了一种名为多视图影响力预测网络（MvInf）的深度学习框架，该框架将多视图学习和图注意力神经网络相结合，以解决社会影响力预测的问题。MvInf将用户的不同属性特征作为图注意力网络的输入，并利用不同视图之间的互补性和一致性来增强学习性能，从而更好地预测用户行为。在四个标准数据集（开放学术图谱，Twitter，微博和Digg）上进行的实验表明，与以前的基于单一视图的方法相比，所提出的MvInf模型可以获得更好的性能。