卫星异质信息网络中节点重要度衡量方法

doi:10.11959/j.issn.2096-8930.2022021

摘要/Abstract

摘要：

卫星网络中不同节点对于网络连通性及吞吐量的影响不同，为了避免卫星网络关键节点失效导致的网络通信失常现象，可通过衡量卫星网络节点重要度提前进行网络规划。针对卫星网络信息异质的特点，定义卫星虚拟节点及虚拟子层，并提出卫星多层异质信息关联网络模型，定义虚拟节点的归一化度数、归一化节点介数及虚拟节点关联因子，并通过所定义参数提出网络节点重要度计算函数，可精准衡量卫星多层异质信息网络中的节点重要度。基于典型实例对网络连通度及吞吐量指标进行分析，结果表明本文所提算法与经典图论算法相比，能够综合考虑数据信息对于网络节点重要度的影响，更精准描述卫星多层异质信息网络的连通度指标。

关键词: 卫星网络, 异质信息, 节点重要度, 网络连通度

Abstract:

In satellite network, different nodes have different effects on network connectivity and throughput.In order to avoid network communication disturbances caused by the failure of key nodes in the satellite network, network planning can be carried out in advance by measuring the importance of satellite network nodes.According to the characteristics of satellite network information heterogeneity, satellite virtual nodes and virtual sub-layers were defined, and the satellite multi-layer heterogeneous information correlation network model was proposed.The normalized degree, normalized node betweenness and virtual node correlation factor of virtual nodes were defi ned, and the network node importance calculation function was proposed through the defined parameters, which could accurately measured the node importance in the satellite multi-layer heterogeneous information network.Based on typical examples, the network connectivity and throughput indicators were analyzed.The results showed that the proposed algorithm was better than the classical graph theory algorithm, which could comprehensively considered the impact of data information on the importance of network nodes, and more accurately described the connectivity index of satellite multi-layer heterogeneous information networks.

Key words: satellite network, heterogeneous information, note importance, network connectivity

中图分类号:

TP927.2

李静玲, 黎军, 梁薇, 崔涛, 张怡. 卫星异质信息网络中节点重要度衡量方法[J]. 天地一体化信息网络, 2022, 3(2): 56-62.

Jingling Li, Jun Li, Wei Liang, Tao Cui, Yi Zhang. Node Importance Measurement Method in Heterogeneous Information Satellite Network[J]. Space-Integrated-Ground Information Networks, 2022, 3(2): 56-62.

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参数	A	B	C	D	E	F	G	H
虚拟节点归一化度K_1i	1/5	1/10	1/10	3/20	1/10	1/10	3/20	1/10
虚拟节点介数D_1i	15/30	11/30	10/30	12/30	8/30	11/30	12/30	7/30

参数	A	B	C	D	E	F	G	H
虚拟节点重要度SIN_i	0.100	0.036	0.033	0.060	0.026	0.036	0.060	0.023

参数	A	B	C	D	E	F	G	H
虚拟节点归一化度K_2i	2/8	1/8	0	0	0	1/8	3/8	1/8
虚拟节点介数D_2i	7/10	3/10	0	0	0	4/10	9/10	4/10

参数	A	B	C	D	E	F	G	H
虚拟节点重要度DIN_i	0.175 0	0.037 5	0	0	0	0.050 0	0.337 5	0.050 0

参数	A	B	C	D	E	F	G	H
虚拟节点关联因子G_i	0.269 2	0.057 6	0	0	0	0.076 9	0.519 2	0.076 9