Zhang Jinhua, Zhang Qishan, Wu Ling, Zhang Jinxin
School of Economics and Management, Fuzhou University, Fuzhou 350108, China.
College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.
Entropy (Basel). 2022 Feb 18;24(2):293. doi: 10.3390/e24020293.
Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.
近年来，识别复杂网络中的有影响力节点吸引了众多研究人员的关注。然而，由于时间复杂度高，基于全局属性的方法已不适用于大规模复杂网络。此外，与仅考虑单一属性的方法相比，考虑多个属性可以提高所用方法的性能。因此，本文提出了一种基于信息熵的新的多局部属性加权中心性（LWC），结合了度和聚类系数；在评估节点影响力和识别复杂网络中的有影响力节点时，同时考虑了一步和两步邻域信息。首先，将复杂网络中节点的影响力分为直接影响力和间接影响力。选择度和聚类系数作为直接影响力度量。其次，基于这两种直接影响力度量，定义了两种间接影响力度量：两跳度和两跳聚类系数。然后，利用信息熵对上述四种影响力度量进行加权，通过计算这些度量的加权和得到每个节点的LWC。最后，根据LWC的值对所有节点进行排序，从而识别出有影响力的节点。将所提出的LWC方法应用于识别四个真实网络中的有影响力节点，并与五种著名方法进行比较。实验结果表明了该方法在判别能力和准确性方面的良好性能。
