About: Optimization of targeted node set in complex networks under percolation and selection

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Optimization of targeted node set in complex networks under percolation and selection
Creator	Liu, Yang Wang, Xi Kurths, Jürgen
Source	PMC
abstract	Most of the existing methods for the robustness and targeted immunization problems can be viewed as greedy strategies, which are quite efficient but readily induce a local optimization. In this paper, starting from a percolation perspective, we develop two strategies, the relationship-related (RR) strategy and the prediction relationship (PR) strategy, to avoid a local optimum only through the investigation of interrelationships among nodes. Meanwhile, RR combines the sum rule and the product rule from explosive percolation, and PR holds the assumption that nodes with high degree are usually more important than those with low degree. In this manner our methods have a better capability to collapse or protect a network. The simulations performed on a number of networks also demonstrate their effectiveness, especially on large real-world networks where RR fragments each of them into the same size of the giant component; however, RR needs only less than [Formula: see text] of the number of nodes which are necessary for the most excellent existing methods.
has issue date	2018-07-23(xsd:dateTime)
bibo:doi	10.1103/physreve.98.012313
bibo:pmid	30110741
has license	no-cc
sha1sum (hex)	86d99e5e3dbe5a7b4eee270446089da6484ac7b0
schema:url	https://doi.org/10.1103/physreve.98.012313
resource representing a document's title	Optimization of targeted node set in complex networks under percolation and selection
has PubMed Central identifier	PMC7217537
has PubMed identifier	30110741
resource representing a document's body	covid:86d99e5e3dbe5a7b4eee270446089da6484ac7b0#body_text
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