English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

Authors
/persons/resource/yangliu

Liu,  Yang
Potsdam Institute for Climate Impact Research;

Wang,  X.
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PIKpublic
Supplementary Material (public)
There is no public supplementary material available
Citation

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


Cite as: https://publications.pik-potsdam.de/pubman/item/item_23151
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.