Artículos de revistas
Role of centrality for the identification of influential spreaders in complex networks
Fecha
2014-09-22Registro en:
Physical Review E, College Park, v.90, n.3, p.032812-1-032812-17, 2014
1539-3755
10.1103/PhysRevE.90.032812
Autor
Arruda, Guilherme Ferraz de
Barbieri, André Luiz
Rodriguez, Pablo Martin
Rodrigues, Francisco Aparecido
Costa, Luciano da Fontoura
Institución
Resumen
The identification of the most influential spreaders in networks is important to control and understand the
spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumorlike
dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the
dynamics being studied. For instance, the key disease spreaders might not necessarily be so important when it
comes to analyzing social contagion or rumor propagation. Additionally, it has been shown that different metrics
(degree, coreness, etc.) might identify different influential nodes even for the same dynamical processes with
diverse degrees of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease
and rumor spreading capabilities of the nodes in different synthetic and real-world (both spatial and nonspatial)
networks. We also propose a generalization of the random walk accessibility as a new centrality measure and
derive analytical expressions for the latter measure for simple network configurations. Our results show that for
nonspatial networks, the k-core and degree centralities are the most correlated to epidemic spreading, whereas the
average neighborhood degree, the closeness centrality, and accessibility are the most related to rumor dynamics.
On the contrary, for spatial networks, the accessibility measure outperforms the rest of the centrality metrics
in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our
analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of
spatial networks.