Identifying influential nodes is a fundamental and open issue in analysis of the complex networks. The measurement of the spreading capabilities of nodes is an attractive challenge in this field. Node centrality is one of the most popular methods used to identify the influential nodes, which includes the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC). The DC is an efficient method but not effective. The BC and CC are effective but not efficient. They have high computational complexity. To balance the effectiveness and efficiency, this paper proposes the neighborhood entropy centrality to rank the influential nodes. The proposed method uses the notion of entropy to improve the DC. For evaluating the performance, the susceptible-infected-recovered model is used to simulate the information spreading process of messages on nine real-world networks. The experimental results reveal the accuracy and efficiency of the proposed method.

中文翻译：

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基于邻域熵中心性识别复杂网络中的影响节点

识别有影响的节点是复杂网络分析中的一个基本且开放的问题。节点传播能力的测量是该领域的一个有吸引力的挑战。节点中心性是最流行的识别影响节点的方法之一，包括度中心性（DC）、介数中心性（BC）和接近中心性（CC）。DC是一种有效的方法，但不是有效的。BC 和 CC 是有效的，但不是有效的。它们具有很高的计算复杂度。为了平衡有效性和效率，本文提出邻域熵中心性对有影响的节点进行排序。所提出的方法使用熵的概念来改进 DC。为了评估性能，易感-感染-恢复模型用于模拟消息在九个真实网络上的信息传播过程。实验结果表明了所提方法的准确性和效率。