20,823 research outputs found
Understanding the spreading power of all nodes in a network: a continuous-time perspective
Centrality measures such as the degree, k-shell, or eigenvalue centrality can
identify a network's most influential nodes, but are rarely usefully accurate
in quantifying the spreading power of the vast majority of nodes which are not
highly influential. The spreading power of all network nodes is better
explained by considering, from a continuous-time epidemiological perspective,
the distribution of the force of infection each node generates. The resulting
metric, the \textit{expected force}, accurately quantifies node spreading power
under all primary epidemiological models across a wide range of archetypical
human contact networks. When node power is low, influence is a function of
neighbor degree. As power increases, a node's own degree becomes more
important. The strength of this relationship is modulated by network structure,
being more pronounced in narrow, dense networks typical of social networking
and weakening in broader, looser association networks such as the Internet. The
expected force can be computed independently for individual nodes, making it
applicable for networks whose adjacency matrix is dynamic, not well specified,
or overwhelmingly large
Cost-efficient vaccination protocols for network epidemiology
We investigate methods to vaccinate contact networks -- i.e. removing nodes
in such a way that disease spreading is hindered as much as possible -- with
respect to their cost-efficiency. Any real implementation of such protocols
would come with costs related both to the vaccination itself, and gathering of
information about the network. Disregarding this, we argue, would lead to
erroneous evaluation of vaccination protocols. We use the
susceptible-infected-recovered model -- the generic model for diseases making
patients immune upon recovery -- as our disease-spreading scenario, and analyze
outbreaks on both empirical and model networks. For different relative costs,
different protocols dominate. For high vaccination costs and low costs of
gathering information, the so-called acquaintance vaccination is the most cost
efficient. For other parameter values, protocols designed for query-efficient
identification of the network's largest degrees are most efficient
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
- …