![]() 7 proposed a new node-centring method called unsigned Laplacian feature vector centring, considering the mutual influence between nodes and their incident edges. Methods in this category include Degree Centrality (DC) 2, Clustering Coefficient 3, K-shell Decomposition 4, Closeness Centrality (CC) 5, Betweenness Centrality (BC) 6.In addition, some new methods have also been proposed recently. Although these methods do not consider the characteristics of the actual Internet, they still have guiding significance for existing research. The research on vital nodes discovery originated from graph theory research based on complex networks. How to obtain accurate regional network topology is of great practical significance for discovering vital nodes in target areas. The routing characteristics of the actual Internet greatly affect the characterization of the vital nodes of regional networks. Although existing vital nodes discovery research has achieved rich results, the research seldom considers the actual routing situation of the Internet. Besides, this work can also provide a reference for optimizing existing network protocols and help the network recover more quickly and efficiently after node failure 1. Vital nodes discovery can mine out the important routing nodes in the network, help network O &M personnel optimize O &M strategies, improve efficiency, and prevent catastrophic failures. On account of limited resources, network O &M personnel would pay more attention to the vital nodes in the network to guarantee the network’s quality of service (QoS). The expansion of the Internet brings unprecedented pressure to network operation and maintenance (O &M). 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. Experiments on the Internet measurement data (275 million probing results collected in 107 days) demonstrate that: the proposed algorithm outperforms four existing typical algorithms. We can evaluate the node importance in a more realistic network structure. Unlike existing algorithms, the proposed algorithm reconstructs the network topology based on communication and transforms unweighted network connections into weighted connections. Finally, we weight the edge based on the actual network’s routing characteristics and discover vital nodes in combination with the weighting degree. 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. We analyze the stability of multiple rounds of measurement results to overcome the single vantage point’s path deviation. In this manuscript, a vital regional routing nodes discovery algorithm based on routing characteristics is proposed. The key is using the Internet’s routing characteristics to remove noisy paths and accurately describe the network topology. Vital node discovery is a hotspot in network topology research.
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