Abstract
Node ranking is an essential problem in complex networks, which aims to identify influential or central nodes in a graph. Existing methods for node ranking are either computationally expensive or suboptimal for different networks and multiple tasks. In this paper, we propose GNR, a graph neural network-based node ranking model that can sort nodes quickly and efficiently for any type of networks. To achieve this, GNR takes into account three factors to calculate the importance of nodes and train the model: degree, infection score, and dismantling score, which capture the local and global structure of the network and the effect of nodes on network propagation and dismantling. We conduct extensive experiments on 16 real networks, and compare GNR with other centrality-based node ranking models on three tasks: network dismantling, virus propagation, and information spreading. The results show that GNR performs better in most cases and validates the effectiveness and superiority of our model.
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