Identifying critical nodes in complex networks via graph convolutional networks
Inspired by the concept of graph convolutional networks, a simply yet effectively method named R C N N is presented to identify critical nodes with the best spreading ability and shows that under Susceptible–Infected–Recovered (SIR) model, this method outperforms the traditional benchmark methods.
Abstract
Critical nodes of complex networks play a crucial role in effective information spreading. There are many methods have been proposed to identify critical nodes in complex networks, ranging from centralities of nodes to diffusion-based processes. Most of them try to find what kind of structure will make the node more influential. In this paper, inspired by the concept of graph convolutional networks(GCNs), we convert the critical node identification problem in complex networks into a regression problem. Considering adjacency matrices of networks and convolutional neural networks(CNNs), a simply yet effectively method named RCNN is presented to identify critical nodes with the best spreading ability. In this approach, we can generate feature matrix for each node and use a convolutional neural network to train and predict the influence of nodes. Experimental results on nine synthetic and fifteen real networks show that under Susceptible–Infected–Recovered (SIR) model, RCNN outperforms the traditional benchmark methods on identifying critical nodes under spreading dynamic.