Global efficiency of local immunization on complex networks

Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6.
Scientific Reports (Impact Factor: 5.58). 07/2013; 3:2171. DOI: 10.1038/srep02171
Source: PubMed


Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

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Available from: Laurent Hébert-Dufresne, Jun 02, 2014
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    • "We have not considered k-core centrality for evaluation as it is shown to be not very effective in finding the influential nodes for targeted immunization.[14] 1. global deg: Degree centrality denotes the number of immediate neighbors of a node, i.e. which are only one edge away from the node. "
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    ABSTRACT: 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.
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    • "Among many ingredients for quick and wide spreading, influential spreaders play a major role [5] [6] [7]. Accordingly, immunization on large-degree nodes (they are usually considered to be more influential) is a highly efficient method to control epidemic spreading [8] [9] [10]. It is of great theoretical and practical significance to identify influential spreaders in networks, and similar methods can be applied in ranking scientists [11] [12], publications [12], athletes [13] and finding influential directors [14]. "
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    ABSTRACT: Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank [L. Lv et al., PLoS ONE 6 (2011) e21202]. According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders, (ii) the higher tolerance to noisy data, and (iii) the higher robustness to intentional attacks.
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    ABSTRACT: Recently, there is a growing interest in how to mitigate epidemic spreading through complex networks such as infection propagation in population, rumor spreading in social interaction, and, malicious attacks in computer networks. Due to high cost and limitation of immunization resources, a well-established strategy is required to select whom to inoculate. In this paper, we propose a new immunization strategy based on stochastic hill-climbing algorithm to find a subset of nodes whose immunization efficiently reduce the network vulnerability to worst-case epidemic size. Our experiments show that SHCI shows up to 31% improvement in real networks and up to 89% in model networks compared to targeted immunization algorithms which immunize nodes based on their centrality.
    Computer and Knowledge Engineering (ICCKE), 2013 3th International eConference on; 01/2013
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