Identifying influential nodes in complex networks based on Neighbours and edges

Identifying the influential nodes is one of the research focuses in network information mining. Many centrality measures used to evaluate influence abilities of nodes can’t balance between high accuracy and low time complexity. The NL centrality based on the neighbors and importance of edges is proposed which considers the second-degree neighbor’s impact on the influence of a node and utilizes the connectivity and unsubstitutability of edge to distinguish topological position of a node. In order to evaluate the accuracy of NL centrality, the SIR model is used to simulate the process of virus propagation in four real-world networks. Experiment results of monotonicity, validity and efficiency demonstrate that the NL centrality has a competitive performance in distinguishing the influence of nodes and it is suitable for large-scale networks because of the high efficiency in computation.