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A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

Qiang Wei1, 2, *, Guangmin Hu1, Chao Shen3, Yunfei Yin4, 5
1 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
2 National Key Laboratory of Science and Technology on Blind Signal Processing, Chengdu, 610041, China.
3 MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, 710049, China.
4 The Electronic Engineering Department, Universidad de Sevilla, Seville, 41004, Spain.
5 School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China.
* Corresponding Author: Qiang Wei. Email: .

Computers, Materials & Continua 2020, 63(2), 705-724. https://doi.org/10.32604/cmc.2020.07467

Received 23 May 2019; Accepted 02 August 2019; Issue published 01 May 2020

Abstract

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring. However, the existing methods are time-consuming for even moderate-scale networks. In this paper, we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries. The effectiveness of the proposed method is validated through synthetic and real-world networks. The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

Keywords

Network discovery, shortest-path cover, shortest-path distance query, large complex networks.

Cite This Article

Q. Wei, G. Hu, C. Shen and Y. Yin, "A fast method for shortest-path cover identification in large complex networks," Computers, Materials & Continua, vol. 63, no.2, pp. 705–724, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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