Influenced node discovery in a temporal contact network based on common nodes.

infection probability influenced nodes information diffusion temporal contact network temporal diffusion path

Journal

Mathematical biosciences and engineering : MBE
ISSN: 1551-0018
Titre abrégé: Math Biosci Eng
Pays: United States
ID NLM: 101197794

Informations de publication

Date de publication:
15 Jun 2023
Historique:
medline: 8 9 2023
pubmed: 8 9 2023
entrez: 7 9 2023
Statut: ppublish

Résumé

Verification is the only way to make sure if a node is influenced or not because of the uncertainty of information diffusion in the temporal contact network. In the previous methods, only $ N $ influenced nodes could be found for a given number of verifications $ N $. The target of discovering influenced nodes is to find more influenced nodes with the limited number of verifications. To tackle this difficult task, the common nodes on the temporal diffusion paths is proposed in this paper. We prove that if a node $ v $ is confirmed as the influenced node and there exist common nodes on the temporal diffusion paths from the initial node to the node $ v $, these common nodes can be regarded as the influenced nodes without verification. It means that it is possible to find more than $ N $ influenced nodes given $ N $ verifications. The common nodes idea is applied to search influenced nodes in the temporal contact network, and three algorithms are designed based on the idea in this paper. The experiments show that our algorithms can find more influenced nodes in the existence of common nodes.

Identifiants

pubmed: 37679106
doi: 10.3934/mbe.2023609
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

13660-13680

Auteurs

Jinjing Huang (J)

School of Software and Services Outsourcing, Suzhou Vocational Institute of Industrial Technology, Suzhou 215004, China.

Xi Wang (X)

School of Software and Services Outsourcing, Suzhou Vocational Institute of Industrial Technology, Suzhou 215004, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.

Classifications MeSH