English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Framework of evolutionary algorithm for investigation of influential nodes in complex networks

Liu, Y., Wang, X., Kurths, J. (2019): Framework of evolutionary algorithm for investigation of influential nodes in complex networks. - IEEE Transactions on Evolutionary Computation, 23, 6, 1049-1063.
https://doi.org/10.1109/TEVC.2019.2901012

Item is

Files

show Files
hide Files
:
8495.pdf (Publisher version), 7MB
 
File Permalink:
-
Name:
8495.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Liu, Yang1, Author              
Wang, X.2, Author
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2019
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TEVC.2019.2901012
PIKDOMAIN: RD4 - Complexity Science
eDoc: 8495
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE Transactions on Evolutionary Computation
Source Genre: Journal, SCI, Scopus
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 23 (6) Sequence Number: - Start / End Page: 1049 - 1063 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-evolutionary-computation