749 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
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
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