Narrow Results

Identifying critical nodes in complex networks is a challenging topic. There are already various crucial node identification methods based on deep learning. However, these methods ignore the interactions between nodes and neighbors when learning node representations, which result in node features learnt insufficient. To solve this problem, we propose a critical node identification model that combines self-attention and ResNet. First, we take degree centrality, closeness centrality, betweenness centrality and clustering coefficient as the features of nodes and use a novel neighbor feature polymerization approach to generate a feature matrix for each node. Then, the susceptible infection recovery (SIR) model is used to simulate the propagation ability of the nodes, and the nodes are categorized based on their propagation ability to acquire their labels. Finally, the feature matrix and labels of the nodes are used as inputs to the model to learn the hidden representation of the nodes. We evaluate the model with accuracy, precision, recall, the F1 index, the ROC curve, and the PR curve in five real networks. The results show that the method outperforms benchmark methods and can effectively identify critical nodes in complex networks.