27,303 research outputs found
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
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
Fragmenting networks by targeting collective influencers at a mesoscopic level
A practical approach to protecting networks against epidemic processes such
as spreading of infectious diseases, malware, and harmful viral information is
to remove some influential nodes beforehand to fragment the network into small
components. Because determining the optimal order to remove nodes is a
computationally hard problem, various approximate algorithms have been proposed
to efficiently fragment networks by sequential node removal. Morone and Makse
proposed an algorithm employing the non-backtracking matrix of given networks,
which outperforms various existing algorithms. In fact, many empirical networks
have community structure, compromising the assumption of local tree-like
structure on which the original algorithm is based. We develop an immunization
algorithm by synergistically combining the Morone-Makse algorithm and coarse
graining of the network in which we regard a community as a supernode. In this
way, we aim to identify nodes that connect different communities at a
reasonable computational cost. The proposed algorithm works more efficiently
than the Morone-Makse and other algorithms on networks with community
structure.Comment: 5 figures, 3 tables, and SI include
Immunization of networks with community structure
In this study, an efficient method to immunize modular networks (i.e.,
networks with community structure) is proposed. The immunization of networks
aims at fragmenting networks into small parts with a small number of removed
nodes. Its applications include prevention of epidemic spreading, intentional
attacks on networks, and conservation of ecosystems. Although preferential
immunization of hubs is efficient, good immunization strategies for modular
networks have not been established. On the basis of an immunization strategy
based on the eigenvector centrality, we develop an analytical framework for
immunizing modular networks. To this end, we quantify the contribution of each
node to the connectivity in a coarse-grained network among modules. We verify
the effectiveness of the proposed method by applying it to model and real
networks with modular structure.Comment: 3 figures, 1 tabl
Boosting the Immunization Workforce: Lessons from the Merck Vaccine Network - Africa
This report shares lessons learned from The Merck Company Foundation's decade of experience building immunization capacity in Africa. The Merck Vaccine Network -- Africa, a philanthropic initiative to train immunization managers in Kenya, Mali, Uganda, and Zambia, suggests seven key lessons that can help other funders, governments, and NGOs designing or implementing similar vaccine delivery training programs improve the effectiveness and sustainability of their work.Merck's experience designing and supporting the initiative can offer valuable lessons for other actors in the immunization and broader global health fields who are engaged in or planning similar work. Specifically, we identify seven forward-looking lessons that can increase the effectiveness and sustainability of programs to build the capacity of the vaccine workforce in developing countries:Conduct a rigorous needs assessment to anchor efforts in local needs and priorities;Perform ongoing monitoring and evaluation (M&E) to enable programs to adapt, improve, and generate evidence of impact to attract new partners and funding;Create a sustainability plan at the outset to ensure that program impact is maintained beyond the conclusion of initial funding;Embed programs into local health systems to ensure that investments leverage existing infrastructure, relationships, and resources, and that impact can be sustained beyond the life of the program;Employ locally-adapted curricula and appropriate teaching techniques to maximize transfer and retention of relevant knowledge;Incorporate supportive supervision into programs to ensure that transferred knowledge is maintained and acted upon;Facilitate and support regular convening and communication, enabling continuous learning for improvement.In addition to describing the approach taken by MVN-A and the results achieved in the four focus countries, this paper provides additional detail on each lesson, supported by case studies from the MVNA experience
Containing epidemic outbreaks by message-passing techniques
The problem of targeted network immunization can be defined as the one of
finding a subset of nodes in a network to immunize or vaccinate in order to
minimize a tradeoff between the cost of vaccination and the final (stationary)
expected infection under a given epidemic model. Although computing the
expected infection is a hard computational problem, simple and efficient
mean-field approximations have been put forward in the literature in recent
years. The optimization problem can be recast into a constrained one in which
the constraints enforce local mean-field equations describing the average
stationary state of the epidemic process. For a wide class of epidemic models,
including the susceptible-infected-removed and the
susceptible-infected-susceptible models, we define a message-passing approach
to network immunization that allows us to study the statistical properties of
epidemic outbreaks in the presence of immunized nodes as well as to find
(nearly) optimal immunization sets for a given choice of parameters and costs.
The algorithm scales linearly with the size of the graph and it can be made
efficient even on large networks. We compare its performance with topologically
based heuristics, greedy methods, and simulated annealing
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
Reactive immunization on complex networks
Epidemic spreading on complex networks depends on the topological structure
as well as on the dynamical properties of the infection itself. Generally
speaking, highly connected individuals play the role of hubs and are crucial to
channel information across the network. On the other hand, static topological
quantities measuring the connectivity structure are independent on the
dynamical mechanisms of the infection. A natural question is therefore how to
improve the topological analysis by some kind of dynamical information that may
be extracted from the ongoing infection itself. In this spirit, we propose a
novel vaccination scheme that exploits information from the details of the
infection pattern at the moment when the vaccination strategy is applied.
Numerical simulations of the infection process show that the proposed
immunization strategy is effective and robust on a wide class of complex
networks
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