OBJECTIVE We examined the implications of illness centrality for psychological and physical health among male and female early adolescents with type 1 diabetes. METHODS We interviewed 132 adolescents before or after a routine clinic appointment. We measured the extent to which they defined themselves in terms of their illness, their views of the illness, psychological well-being, self-care be...

With great theoretical and practical significance, locating influential nodes of complex networks is a promising issue. In this paper, we present a dynamics-sensitive (DS) centrality by integrating topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptib...

How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gr...

The relationships among the members of a population can be visualized using individual networks, where each individual is a node connected to each other by means of links describing the interactions. The centrality of a given node captures its importance within the network. We hypothesize that in mutualistic networks, the centrality of a node should benefit its fitness. We test this idea studyi...

Abstract Centrality, in some sense, captures the extent to which a vertex controls flow of information network. Here, we propose Local Detour Centrality as novel centrality-based betweenness measure that shortens paths between neighboring vertices compared alternative paths. After presenting our measure, demonstrate empirically it differs from other leading central measures, such betweenness, d...

We study network centrality based on dynamic influence propagation models in social networks. To illustrate our integrated mathematical-algorithmic approach for understanding the fundamental interplay between dynamic influence processes and static network structures, we focus on two basic centrality measures: (a) Single Node Influence (SNI) centrality, which measures each node’s significance by...

We propose a method for the Distributed Assessment of Network CEntrality (DANCE) in complex networks. DANCE attributes to each node a volume-based centrality computed using only localized information, thus not requiring knowledge of the full network topology. We show DANCE is simple, yet efficient, in assessing node centrality in a distributed way. Our proposal also provides a way for locating ...