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一种基于稳定路径分析的区域网络关键节点发现算法。

An algorithm for discovering vital nodes in regional networks based on stable path analysis.

作者信息

Liu Yan, Liu Yimin, Liu Fenlin, Fan Jiaxing, Tao Zhiyuan

机构信息

State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China.

Key Laboratory of Cyberspace Situation Awareness of Henan Province, Zhengzhou, 450001, China.

出版信息

Sci Rep. 2023 Sep 16;13(1):15395. doi: 10.1038/s41598-023-39174-7.

DOI:10.1038/s41598-023-39174-7
PMID:37717092
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10505182/
Abstract

Vital node discovery is a hotspot in network topology research. The key is using the Internet's routing characteristics to remove noisy paths and accurately describe the network topology. In this manuscript, a vital regional routing nodes discovery algorithm based on routing characteristics is proposed. We analyze the stability of multiple rounds of measurement results to overcome the single vantage point's path deviation. The unstable paths are eliminated from the regional network which is constructed through probing for target area, and the pruned topology is more in line with real routing rules. Finally, we weight the edge based on the actual network's routing characteristics and discover vital nodes in combination with the weighting degree. Unlike existing algorithms, the proposed algorithm reconstructs the network topology based on communication and transforms unweighted network connections into weighted connections. We can evaluate the node importance in a more realistic network structure. Experiments on the Internet measurement data (275 million probing results collected in 107 days) demonstrate that: the proposed algorithm outperforms four existing typical algorithms. Among 15 groups of comparison in 3 cities, our algorithm found more (or the same number) backbone nodes in 10 groups and found more (or the same number) national backbone nodes in 13 groups.

摘要

关键节点发现是网络拓扑研究中的一个热点。关键在于利用互联网的路由特性去除噪声路径并准确描述网络拓扑。在本论文中,提出了一种基于路由特性的关键区域路由节点发现算法。我们分析多轮测量结果的稳定性以克服单视角路径偏差。从不稳定路径从通过探测目标区域构建的区域网络中消除,修剪后的拓扑更符合实际路由规则。最后,我们根据实际网络的路由特性对边进行加权,并结合加权程度发现关键节点。与现有算法不同,所提算法基于通信重建网络拓扑并将未加权的网络连接转换为加权连接。我们可以在更真实的网络结构中评估节点重要性。对互联网测量数据(107天内收集的2.75亿个探测结果)进行的实验表明:所提算法优于四种现有的典型算法。在3个城市的15组比较中,我们的算法在10组中发现了更多(或相同数量)的骨干节点,在13组中发现了更多(或相同数量)的国家骨干节点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/a5cce67dee88/41598_2023_39174_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/9b5e81386cee/41598_2023_39174_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/6efa0f531abd/41598_2023_39174_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/e1d2a2c90961/41598_2023_39174_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/cfbd175be126/41598_2023_39174_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/4db184a619ca/41598_2023_39174_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/3577897b567a/41598_2023_39174_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/a19e39cf869c/41598_2023_39174_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/45e289b82f76/41598_2023_39174_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/a5cce67dee88/41598_2023_39174_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/9b5e81386cee/41598_2023_39174_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/6efa0f531abd/41598_2023_39174_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/e1d2a2c90961/41598_2023_39174_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/cfbd175be126/41598_2023_39174_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/4db184a619ca/41598_2023_39174_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/3577897b567a/41598_2023_39174_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/a19e39cf869c/41598_2023_39174_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/45e289b82f76/41598_2023_39174_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/392d/10505182/a5cce67dee88/41598_2023_39174_Fig9_HTML.jpg

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