19,768 research outputs found
Community-based Immunization Strategies for Epidemic Control
Understanding the epidemic dynamics, and finding out efficient techniques to
control it, is a challenging issue. A lot of research has been done on targeted
immunization strategies, exploiting various global network topological
properties. However, in practice, information about the global structure of the
contact network may not be available. Therefore, immunization strategies that
can deal with a limited knowledge of the network structure are required. In
this paper, we propose targeted immunization strategies that require
information only at the community level. Results of our investigations on the
SIR epidemiological model, using a realistic synthetic benchmark with
controlled community structure, show that the community structure plays an
important role in the epidemic dynamics. An extensive comparative evaluation
demonstrates that the proposed strategies are as efficient as the most
influential global centrality based immunization strategies, despite the fact
that they use a limited amount of information. Furthermore, they outperform
alternative local strategies, which are agnostic about the network structure,
and make decisions based on random walks.Comment: 6 pages, 7 figure
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Centrality Measures for Networks with Community Structure
Understanding the network structure, and finding out the influential nodes is
a challenging issue in the large networks. Identifying the most influential
nodes in the network can be useful in many applications like immunization of
nodes in case of epidemic spreading, during intentional attacks on complex
networks. A lot of research is done to devise centrality measures which could
efficiently identify the most influential nodes in the network. There are two
major approaches to the problem: On one hand, deterministic strategies that
exploit knowledge about the overall network topology in order to find the
influential nodes, while on the other end, random strategies are completely
agnostic about the network structure. Centrality measures that can deal with a
limited knowledge of the network structure are required. Indeed, in practice,
information about the global structure of the overall network is rarely
available or hard to acquire. Even if available, the structure of the network
might be too large that it is too much computationally expensive to calculate
global centrality measures. To that end, a centrality measure is proposed that
requires information only at the community level to identify the influential
nodes in the network. Indeed, most of the real-world networks exhibit a
community structure that can be exploited efficiently to discover the
influential nodes. We performed a comparative evaluation of prominent global
deterministic strategies together with stochastic strategies with an available
and the proposed deterministic community-based strategy. Effectiveness of the
proposed method is evaluated by performing experiments on synthetic and
real-world networks with community structure in the case of immunization of
nodes for epidemic control.Comment: 30 pages, 4 figures. Accepted for publication in Physica A. arXiv
admin note: text overlap with arXiv:1411.627
Optimal curing policy for epidemic spreading over a community network with heterogeneous population
The design of an efficient curing policy, able to stem an epidemic process at
an affordable cost, has to account for the structure of the population contact
network supporting the contagious process. Thus, we tackle the problem of
allocating recovery resources among the population, at the lowest cost possible
to prevent the epidemic from persisting indefinitely in the network.
Specifically, we analyze a susceptible-infected-susceptible epidemic process
spreading over a weighted graph, by means of a first-order mean-field
approximation. First, we describe the influence of the contact network on the
dynamics of the epidemics among a heterogeneous population, that is possibly
divided into communities. For the case of a community network, our
investigation relies on the graph-theoretical notion of equitable partition; we
show that the epidemic threshold, a key measure of the network robustness
against epidemic spreading, can be determined using a lower-dimensional
dynamical system. Exploiting the computation of the epidemic threshold, we
determine a cost-optimal curing policy by solving a convex minimization
problem, which possesses a reduced dimension in the case of a community
network. Lastly, we consider a two-level optimal curing problem, for which an
algorithm is designed with a polynomial time complexity in the network size.Comment: to be published on Journal of Complex Network
Data on face-to-face contacts in an office building suggests a low-cost vaccination strategy based on community linkers
Empirical data on contacts between individuals in social contexts play an
important role in providing information for models describing human behavior
and how epidemics spread in populations. Here, we analyze data on face-to-face
contacts collected in an office building. The statistical properties of
contacts are similar to other social situations, but important differences are
observed in the contact network structure. In particular, the contact network
is strongly shaped by the organization of the offices in departments, which has
consequences in the design of accurate agent-based models of epidemic spread.
We consider the contact network as a potential substrate for infectious disease
spread and show that its sparsity tends to prevent outbreaks of rapidly
spreading epidemics. Moreover, we define three typical behaviors according to
the fraction of links each individual shares outside its own department:
residents, wanderers and linkers. Linkers () act as bridges in the
network and have large betweenness centralities. Thus, a vaccination strategy
targeting linkers efficiently prevents large outbreaks. As such a behavior may
be spotted a priori in the offices' organization or from surveys, without the
full knowledge of the time-resolved contact network, this result may help the
design of efficient, low-cost vaccination or social-distancing strategies
Griffiths phases in infinite-dimensional, non-hierarchical modular networks
Griffiths phases (GPs), generated by the heterogeneities on modular networks,
have recently been suggested to provide a mechanism, rid of fine parameter
tuning, to explain the critical behavior of complex systems. One conjectured
requirement for systems with modular structures was that the network of modules
must be hierarchically organized and possess finite dimension. We investigate
the dynamical behavior of an activity spreading model, evolving on
heterogeneous random networks with highly modular structure and organized
non-hierarchically. We observe that loosely coupled modules act as effective
rare-regions, slowing down the extinction of activation. As a consequence, we
find extended control parameter regions with continuously changing dynamical
exponents for single network realizations, preserved after finite size
analyses, as in a real GP. The avalanche size distributions of spreading events
exhibit robust power-law tails. Our findings relax the requirement of
hierarchical organization of the modular structure, which can help to
rationalize the criticality of modular systems in the framework of GPs.Comment: 14 pages, 8 figure
- …