559 research outputs found
Predicting epidemic outbreak from individual features of the spreaders
Knowing which individuals can be more efficient in spreading a pathogen
throughout a determinate environment is a fundamental question in disease
control. Indeed, over the last years the spread of epidemic diseases and its
relationship with the topology of the involved system have been a recurrent
topic in complex network theory, taking into account both network models and
real-world data. In this paper we explore possible correlations between the
heterogeneous spread of an epidemic disease governed by the
susceptible-infected-recovered (SIR) model, and several attributes of the
originating vertices, considering Erd\"os-R\'enyi (ER), Barab\'asi-Albert (BA)
and random geometric graphs (RGG), as well as a real case of study, the US Air
Transportation Network that comprises the US 500 busiest airports along with
inter-connections. Initially, the heterogeneity of the spreading is achieved
considering the RGG networks, in which we analytically derive an expression for
the distribution of the spreading rates among the established contacts, by
assuming that such rates decay exponentially with the distance that separates
the individuals. Such distribution is also considered for the ER and BA models,
where we observe topological effects on the correlations. In the case of the
airport network, the spreading rates are empirically defined, assumed to be
directly proportional to the seat availability. Among both the theoretical and
the real networks considered, we observe a high correlation between the total
epidemic prevalence and the degree, as well as the strength and the
accessibility of the epidemic sources. For attributes such as the betweenness
centrality and the -shell index, however, the correlation depends on the
topology considered.Comment: 10 pages, 6 figure
A measure of individual role in collective dynamics
Identifying key players in collective dynamics remains a challenge in several
research fields, from the efficient dissemination of ideas to drug target
discovery in biomedical problems. The difficulty lies at several levels: how to
single out the role of individual elements in such intermingled systems, or
which is the best way to quantify their importance. Centrality measures
describe a node's importance by its position in a network. The key issue
obviated is that the contribution of a node to the collective behavior is not
uniquely determined by the structure of the system but it is a result of the
interplay between dynamics and network structure. We show that dynamical
influence measures explicitly how strongly a node's dynamical state affects
collective behavior. For critical spreading, dynamical influence targets nodes
according to their spreading capabilities. For diffusive processes it
quantifies how efficiently real systems may be controlled by manipulating a
single node.Comment: accepted for publication in Scientific Report
Dynamic communicability and epidemic spread: a case study on an empirical dynamic contact network
We analyze a recently proposed temporal centrality measure applied to an
empirical network based on person-to-person contacts in an emergency department
of a busy urban hospital. We show that temporal centrality identifies a
distinct set of top-spreaders than centrality based on the time-aggregated
binarized contact matrix, so that taken together, the accuracy of capturing
top-spreaders improves significantly. However, with respect to predicting
epidemic outcome, the temporal measure does not necessarily outperform less
complex measures. Our results also show that other temporal markers such as
duration observed and the time of first appearance in the the network can be
used in a simple predictive model to generate predictions that capture the
trend of the observed data remarkably well.Comment: 31 pages, 15 figures, 11 tables; typos corrected; references added;
Figure 3 added; some changes to the conclusion and introductio
Top influencers can be identified universally by combining classical centralities
Information flow, opinion, and epidemics spread over structured networks.
When using individual node centrality indicators to predict which nodes will be
among the top influencers or spreaders in a large network, no single centrality
has consistently good ranking power. We show that statistical classifiers using
two or more centralities as input are instead consistently predictive over many
diverse, static real-world topologies. Certain pairs of centralities cooperate
particularly well in statistically drawing the boundary between the top
spreaders and the rest: local centralities measuring the size of a node's
neighbourhood benefit from the addition of a global centrality such as the
eigenvector centrality, closeness, or the core number. This is, intuitively,
because a local centrality may rank highly some nodes which are located in
dense, but peripheral regions of the network---a situation in which an
additional global centrality indicator can help by prioritising nodes located
more centrally. The nodes selected as superspreaders will usually jointly
maximise the values of both centralities. As a result of the interplay between
centrality indicators, training classifiers with seven classical indicators
leads to a nearly maximum average precision function (0.995) across the
networks in this study.Comment: 14 pages, 10 figures, 4 supplementary figure
A General Framework for Complex Network Applications
Complex network theory has been applied to solving practical problems from
different domains. In this paper, we present a general framework for complex
network applications. The keys of a successful application are a thorough
understanding of the real system and a correct mapping of complex network
theory to practical problems in the system. Despite of certain limitations
discussed in this paper, complex network theory provides a foundation on which
to develop powerful tools in analyzing and optimizing large interconnected
systems.Comment: 8 page
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Detecting spreading outbreaks in social networks with sensors is of great
significance in applications. Inspired by the formation mechanism of human's
physical sensations to external stimuli, we propose a new method to detect the
influence of spreading by constructing excitable sensor networks. Exploiting
the amplifying effect of excitable sensor networks, our method can better
detect small-scale spreading processes. At the same time, it can also
distinguish large-scale diffusion instances due to the self-inhibition effect
of excitable elements. Through simulations of diverse spreading dynamics on
typical real-world social networks (facebook, coauthor and email social
networks), we find that the excitable senor networks are capable of detecting
and ranking spreading processes in a much wider range of influence than other
commonly used sensor placement methods, such as random, targeted, acquaintance
and distance strategies. In addition, we validate the efficacy of our method
with diffusion data from a real-world online social system, Twitter. We find
that our method can detect more spreading topics in practice. Our approach
provides a new direction in spreading detection and should be useful for
designing effective detection methods
Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities
Identifying important nodes for disease spreading is a central topic in
network epidemiology. We investigate how well the position of a node,
characterized by standard network measures, can predict its epidemiological
importance in any graph of a given number of nodes. This is in contrast to
other studies that deal with the easier prediction problem of ranking nodes by
their epidemic importance in given graphs. As a benchmark for epidemic
importance, we calculate the exact expected outbreak size given a node as the
source. We study exhaustively all graphs of a given size, so do not restrict
ourselves to certain generative models for graphs, nor to graph data sets. Due
to the large number of possible nonisomorphic graphs of a fixed size, we are
limited to 10-node graphs. We find that combinations of two or more
centralities are predictive ( scores of 0.91 or higher) even for the most
difficult parameter values of the epidemic simulation. Typically, these
successful combinations include one normalized spectral centralities (such as
PageRank or Katz centrality) and one measure that is sensitive to the number of
edges in the graph
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