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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Liu, Yang [1, 2] ; Wang, Xi [3] ; Kurths, Jurgen [4, 5, 2]
Número total de Autores: 3
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Fonte: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; v. 23, n. 6, p. 1049-1063, DEC 2019.
Citações Web of Science: 1

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)

Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
Beneficiário:Elbert Einstein Nehrer Macau
Linha de fomento: Auxílio à Pesquisa - Temático