Efficient Approximation Algorithms for Several Positive Influence Dominating Set Problems in Social Networks

Identifying positive influence dominating set (PIDS) with the smallest cardinality can produce positive effect with the minimal cost on a social network. The purpose of this article is to propose new approximation algorithms for the minimum PIDS problem and its variants such as the minimum connected...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on computational social systems Jg. 12; H. 5; S. 2930 - 2939
Hauptverfasser: Zhong, Hao, Li, Weisheng, Zhang, Qi, Lin, Ronghua, Tang, Yong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation
Approximation algorithm
Approximation algorithms
connected positive influence dominating set (CPIDS)
Costs
Drugs
Electronic mail
Heuristic algorithms
Multiplexing
positive influence dominating set (PIDS)
Social networking (online)
Social networks
submodular function
Technological innovation
Time complexity
Training
ISSN:2329-924X, 2373-7476
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Identifying positive influence dominating set (PIDS) with the smallest cardinality can produce positive effect with the minimal cost on a social network. The purpose of this article is to propose new approximation algorithms for the minimum PIDS problem and its variants such as the minimum connected PIDS and the minimum PIDS of multiplex networks, with the aim of finding target sets with smaller cardinality. Through the design of novel submodular potential function, we theoretically prove that new approximation algorithms yield approximation ratios with same order compared with existing algorithms. We further demonstrate the performance of our algorithm by showcasing its efficacy on several real-world and publicly available instances of social networks, thereby providing additional evidence that our proposed algorithm can identify PIDS with smaller cardinality.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2025.3541230