Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model
Yükleniyor...
Dosyalar
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
King Saud bin Abdulaziz University
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Epidemic modeling in complex networks is a hot research topic in recent years. The spreading of a virus (such as SARS-CoV-2) in a community, spreading computer viruses in communication networks, or spreading gossip on a social network is the subject of epidemic modeling. The Susceptible-Infectious-Recovered (SIR) is one of the most popular epidemic models. One crucial issue in epidemic modeling is the determination of the spreading ability of the nodes. Thus, for example, super spreaders can be detected in the early stages. However, the SIR is a stochastic model, and it needs heavy Monte-Carlo simulations. Hence, the researchers focused on combining several centrality measures to distinguish the spreading capabilities of nodes. In this study, we proposed a new method called Lexical Sorting Centrality (LSC), which combines multiple centrality measures. The LSC uses a sorting mechanism similar to lexical sorting to combine various centrality measures for ranking nodes. We conducted experiments on six datasets using SIR to evaluate the performance of LSC and compared LSC with degree centrality (DC), eigenvector centrality (EC), closeness centrality (CC), betweenness centrality (BC), and Gravitational Centrality (GC). Experimental results show that LSC distinguishes the spreading ability of nodes more accurately, more decisively, and faster. © 2021 The Author
Açıklama
Anahtar Kelimeler
Centrality measure, Complex networks, Epidemic modeling, Social networks, Super spreader, Susceptible-Infectious-Recovered model
Kaynak
Journal of King Saud University - Computer and Information Sciences
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
