Identify influential nodes in social networks with graph multi-head attention regression model

Identifying influential nodes in social networks is a fundamental task. Due to the development of Graph Neural Networks, Graph Convolution Network (GCN) based model has been introduced to solve this problem. Compared to traditional methods, the existing GCN-based models are more accurate in identifying influential nodes because they can better aggregate the multi-dimension features. However, the GCN-based method treats this problem as a binary classification task rather than a regression task, making it less practical. To make the GCN-based model more practical, we treat identifying influential nodes as a regression task. Moreover, when aggregating neighbor features, GCN ignores the difference in neighbor importance, which will affect the prediction performance of the GCN-based models. This paper proposes a graph multi-head attention regression model to address these problems. Vast experiments on twelve real-world social networks demonstrate that the proposed model significantly outperforms baseline methods. To the best of our knowledge, this is the first work to introduce the multi-head attention mechanism to identify influential nodes in social networks.