基于图论方法的光通信网络关键节点确定

Key node determination of optical communication network based on graph theory method

为提升网络连通性能,对基于图论方法的光通信网络关键节点确定方法进行研究。依据图论方法建立光通信网络图论模型,利用自适应蚁群算法,求解图论模型,获取节点间的最优路径,优化光通信网络;利用基于局部中心性的关键节点确定算法,按照局部中心性指标选择网络初始点覆盖集,并剔除该点覆盖集,以迭代方式,选取令网络连通节点对提升最小的节点,将其回添至优化网络内,以点覆盖集内节点符合待剔除关键节点数为止,完成光通信网络关键节点确定。实验证明:该方法可有效优化光通信网络,提升网络连通性能;在无向无权与无向加权网络时,该方法均可精准确定关键节点,选择局部度和中心性指标作为关键节点确定的局部中心性指标,可提升关键节点确定精度。

In order to improve the network connectivity performance,this paper studies the method of determining the key nodes of optical communication network based on graph theory.The graph theory model of optical communication network is established according to the graph theory method,and the adaptive ant colony algorithm is used to solve the graph theory model,obtain the optimal path between nodes and optimize the optical communication network;Using the key node determination algorithm based on local centrality,the initial point coverage set of the network is selected according to the local centrality index,and the point coverage set is eliminated.In an iterative way,the node that minimizes the improvement of the network connected node pair is selected and added back to the optimized network.The key nodes of the optical communication network are determined until the nodes in the point coverage set meet the number of key nodes to be eliminated.Experiments show that this method can effectively optimize the optical communication network and improve the connectivity of the network;In the undirected and undirected weighted networks,this method can accurately determine the key nodes,and select the local degree and centrality indicators as the local centrality indicators determined by the key nodes,which can improve the determination accuracy of the key nodes.