度联合信息熵的网络重要节点识别

Node-important Identification Algorithm Based on Degree and Information Entropy

随着网络科学的快速发展,复杂网络中重要节点识别已是一个重要的研究热点。为了突出邻节点局部结构对节点重要性的影响,本文提出度联合信息熵的网络重要节点识别算法,引入信息熵作为权重来量化各邻节点对节点重要性的贡献,并考虑节点及其邻节点的度值来反映节点在网络中的重要性。在8个真实网络上进行了大量实验。实验结果表明,DE算法相比于其他5种算法能更好地识别网络中的重要节点。

With the rapid development of network science,the identification of important nodes in complex networks has become an important research hotspot.In order to highlight the influence of the local structure of adjacent nodes on the importance of nodes,this paper proposes a network important node identification algorithm based on degree and information entropy,introduces information entropy as a weight to quantify the contribution of each adjacent node to the importance of nodes,and considers the degree of nodes and their adjacent nodes to reflect the importance of nodes in the network.A large number of experiments were conducted on 8 real networks.The experimental results show that compared to the other five algorithms,our algorithm can better identify important nodes in the network.