| 摘 要： | How to accurately and effectively identify the influential nodes in networks has always been a core issue. In order to evaluate the importance of the nodes, some famous centrality measures have been proposed and widely used. However, the existing methods still exist some shortcomings. In this work, a novel method named average edge distance contribution (AEDC) is proposed to effectively find the influential nodes, which measures the average contribution of each edge to the sum of distances of all node pairs in the network. For each node, we utilize the relative change of AEDC by removing it from the network to determine its influence. For verifying the effectiveness and feasibility of the AEDC method, we simulate the process of disease spreading in four real complex networks with the Susceptible Infected Recovered (SIR) model. The experimental results show that our proposed method is more accurate than several benchmark centrality measures in terms of identifying the influential nodes.
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