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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Framework of Evolutionary Algorithm for Investigation of Influential Nodes in Complex Networks

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Author(s):
Liu, Yang [1, 2] ; Wang, Xi [3] ; Kurths, Jurgen [4, 5, 2]
Total Authors: 3
Affiliation:
[1] Tech Univ Berlin, Dept Comp Sci, D-10587 Berlin - Germany
[2] Potsdam Inst Climate Impact Res, Dept Complex Sci, D-14473 Potsdam - Germany
[3] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong - Peoples R China
[4] Humboldt Univ, Dept Phys, D-12489 Berlin - Germany
[5] Saratov NG Chernyshevskii State Univ, Saratov 410012 - Russia
Total Affiliations: 5
Document type: Journal article
Source: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; v. 23, n. 6, p. 1049-1063, DEC 2019.
Web of Science Citations: 1
Abstract

There are many target methods that are efficient to tackle the robustness and immunization problem, in particular, to identify the most influential nodes in a certain complex network. Unfortunately, owing to the diversity of networks, none of them could be accounted as a universal approach that works well in a wide variety of networks. Hence, in this paper, from a percolation perspective, we connect the immunization and robustness problem with an evolutionary algorithm, i.e., a framework of an evolutionary algorithm for investigation of influential nodes in complex networks, in which we have developed procedures of selection, mutation, and initialization of population as well as maintaining the diversity of population. To validate the performance of the proposed framework, we conduct intensive experiments on a large number of networks and compare it to several state-of-the-art strategies. The results demonstrate that the proposed method has significant advantages over others, especially on empirical networks in most of which our method has over 10 advantages of both optimal immunization threshold and average giant fraction, even against the most excellent existing strategies. Additionally, our discussion reveals that there might be better solutions with various initial methods. (AU)

FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support Opportunities: Research Projects - Thematic Grants