Identifying influential nodes in complex contagion mechanism

Abstract

Identifying influential nodes in complex networks is one of the most important and challenging problems to help optimize the network structure, control the spread of the epidemic and accelerate the spread of information. In a complex network, the node with the strongest propagation capacity is known as the most influential node from the perspective of propagation. In recent years, identifying the key nodes in complex networks has received increasing attention. However, it is still a challenge to design a metric that has low computational complexity but can accurately identify important network nodes. Currently, many centrality metrics used to evaluate the influence capability of nodes cannot balance between high accuracy and low time complexity. Local centrality suffers from accuracy problems, while global metrics require higher time complexity, which is inefficient for large scale networks. In contrast, semi-local metrics are with higher accuracy and lower time cost. In this paper, we propose a new semi-local centrality measure for identifying influential nodes under complex contagion mechanisms. It uses the higher-order structure within the first and second-order neighborhoods of nodes to define the importance of nodes with near linear time complexity, which can be applied to large-scale networks. To verify the accuracy of the proposed metric, we simulated the disease propagation process in four real and two artificial networks using the SI model under complex propagation. The simulation results show that the proposed method can identify the nodes with the strongest propagation ability more effectively and accurately than other current node importance metrics.