为解决许多关键节点识别算法在评估网络节点重要性时,忽视节点与其邻居节点间的相互关系,导致对网络鲁棒性和脆弱性的评估结果不准确的问题,提出一种改良的局部加权密度度量方式CPR-WCCN,旨在以较低的计算成本准确识别复杂网络中的关键节点.首先,借助节点间的最短路径长度和数量,定义节点间的通信概率序列.其次,通过结合通信概率和相对熵(Communication Probability and Relative Entropy,CPR),将传统的二元邻接矩阵转化为网络归一化相关矩阵.再次,结合加权聚类系数和邻居节点的影响(Weighted Clustering Coefficients and Neighbor Influence,WCCN),得到改进的考虑邻居影响的局部加权密度.最后,为验证CPR-WCCN算法的效果,在故意攻击和随机攻击下进行模拟实验,利用传播模型在4种实际网络上对CPR-WCCN与其他5种算法进行对比分析.实验结果表明:当网络遭受故意攻击,导致前15个关键节点失效时,网络的连通性、效率、最大连接子图以及自然连通性等关键指标较随机攻击出现了更显著的下降;相较于其他5种算法,CPR-WCCN算法表现出最优的整体性能,能够准确且高效地识别出网络中的关键节点.
A novel algorithm for identifying key nodes in complex networks based on relative entropy and neighbor influence clustering
To address the issue of many key node identification algorithms overlooking the interrelation-ships between nodes and their neighbors when assessing node importance in networks,thereby affect-ing the evaluation of network robustness and vulnerability,an improved local weighted density mea-sure is proposed,named CPR-WCCN. This method aims to accurately identify critical nodes in com-plex networks at a lower computational cost. Firstly,a communication probability sequence between nodes is defined using the shortest path lengths and counts. Secondly,the traditional binary adjacency matrix is transformed into a network normalized correlation matrix by combining Communication Prob-ability and Relative Entropy (CPR).Then,by incorporating Weighted Clustering Coefficients and Neighbor Influence (WCCN),an improved local weighted density is derived,which accounts for neighbor influence. Finally,to validate the effectiveness of the CPR-WCCN algorithm,simulation ex-periments are conducted under both intentional and random attacks. Utilizing a propagation model,a comparative analysis of CPR-WCCN is performed against five other algorithms across four real-world networks. The experimental results indicate that under intentional attacks,where the top 15 critical nodes are disabled,key metrics such as network connectivity,efficiency,maximum connected sub-graph,and natural connectivity show a more significant decline than random attacks. Compared to the other five algorithms,the CPR-WCCN algorithm demonstrates optimal overall performance,which accurately and efficiently identifies critical nodes within the network.
万方数据
维普
