Exact solution of bond percolation on small arbitrary graphs

We introduce a set of iterative equations that exactly solves the size distribution of components on small arbitrary graphs after the random removal of edges. We also demonstrate how these equations can be used to predict the distribution of the node partitions (i.e., the constrained distribution of the size of each component) in undirected graphs. Besides opening the way to the theoretical prediction of percolation on arbitrary graphs of large but finite size, we show how our results find application in graph theory, epidemiology, percolation and fragmentation theory.Comment: 5 pages and 3 figure

Adaptive networks: coevolution of disease and topology

Adaptive networks have been recently introduced in the context of disease propagation on complex networks. They account for the mutual interaction between the network topology and the states of the nodes. Until now, existing models have been analyzed using low-complexity analytic formalisms, revealing nevertheless some novel dynamical features. However, current methods have failed to reproduce with accuracy the simultaneous time evolution of the disease and the underlying network topology. In the framework of the adaptive SIS model of Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)], we introduce an improved compartmental formalism able to handle this coevolutionary task successfully. With this approach, we analyze the interplay and outcomes of both dynamical elements, process and structure, on adaptive networks featuring different degree distributions at the initial stage.Comment: 11 pages, 8 figures, 1 appendix. To be published in Physical Review

Strategic tradeoffs in competitor dynamics on adaptive networks

Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.Comment: 20 pages (11 pages for the main text and 9 pages of supplementary material

Modeling the dynamical interaction between epidemics on overlay networks

Epidemics seldom occur as isolated phenomena. Typically, two or more viral agents spread within the same host population and may interact dynamically with each other. We present a general model where two viral agents interact via an immunity mechanism as they propagate simultaneously on two networks connecting the same set of nodes. Exploiting a correspondence between the propagation dynamics and a dynamical process performing progressive network generation, we develop an analytic approach that accurately captures the dynamical interaction between epidemics on overlay networks. The formalism allows for overlay networks with arbitrary joint degree distribution and overlap. To illustrate the versatility of our approach, we consider a hypothetical delayed intervention scenario in which an immunizing agent is disseminated in a host population to hinder the propagation of an undesirable agent (e.g. the spread of preventive information in the context of an emerging infectious disease).Comment: Accepted for publication in Phys. Rev. E. 15 pages, 7 figure

Propagation dynamics on networks featuring complex topologies

Analytical description of propagation phenomena on random networks has flourished in recent years, yet more complex systems have mainly been studied through numerical means. In this paper, a mean-field description is used to coherently couple the dynamics of the network elements (nodes, vertices, individuals...) on the one hand and their recurrent topological patterns (subgraphs, groups...) on the other hand. In a SIS model of epidemic spread on social networks with community structure, this approach yields a set of ODEs for the time evolution of the system, as well as analytical solutions for the epidemic threshold and equilibria. The results obtained are in good agreement with numerical simulations and reproduce random networks behavior in the appropriate limits which highlights the influence of topology on the processes. Finally, it is demonstrated that our model predicts higher epidemic thresholds for clustered structures than for equivalent random topologies in the case of networks with zero degree correlation.Comment: 10 pages, 5 figures, 1 Appendix. Published in Phys. Rev. E (mistakes in the PRE version are corrected here

Le raisonnement à base de logique propositionnelle à l'appui de la fusion et de la révision de bases de données géospatiales

Le but de ce mémoire était d’effectuer, dans un contexte géospatial, une comparai- son d’une approche de raisonnement qualitatif basée sur le PROLOG avec une autre approche reposant sur l’ASP. La principale question que nous posons est la suivante : Le moteur de raisonnement Smodels rendant possible la mise en oeuvre du raisonnement non monotone poussé et faisant intervenir le concept de modèle stable peut-il nous permettre de résoudre des problèmes de vérification de cohérence ontologique et des problèmes de révision dans le contexte de la géomatique ? Pour y répondre, nous avons procédé à une série de tests sur un échantillon de la Base nationale de données topographiques (BNDT). À la lumière des résultats obtenus, cette approche se montre très efficace et contribue à l’amélioration de la cohérence de l’information géospatiale et du raisonnement spatial réalisé à partir de cette dernière.The objective of this thesis is to make a comparison between a qualitative reasoning approach based on PROLOG with another approach based on ASP. Our principal research question was the following : Can the Smodels reasoning engine, allowing for advanced non monotonic reasoning and introducing the stable model concept, allow us to solve ontological consistency checking problems as well as revision problems in a geomatic context ? To answer this question, we carried out a series of tests on a cross-section from the National Topographical Database (NTDB). In the light of the results obtained, this approach has proven very effective and contributes to the amelioration of geospatial information consistency and to the resultant improvement in spatial reasoning

SensLAB Very Large Scale Open Wireless Sensor Network Testbed

International audienceThis paper presents a precise description of SensLAB: Very Large Scale Open Wireless Sensor Network Testbed that has been developed and deployed in order to allow the evaluation of scalable wireless sensor network protocols and applications. SensLAB's main and most important goal is to o er an accurate open access multi-users scienti c tool to support the design, development, tuning, and experimentation of real large-scale sensor network applications. The SensLAB testbed is composed of 1024 nodes and it is distributed among 4 sites. Two sites o er access to mobile nodes. Every sensor node is also able to be con gured as a sink node and can exchange data with any other sink node of the whole SensLAB testbed (locally or remotely) or any computer on the Internet. The hardware designed on purpose and software architectures that allow to reserve, con gure, deploy embedded software, boot wireless sensor nodes and gather experimental data and monitoring information are described in details. We also present short demonstration examples to illustrate the use of the SensLAB testbed

CARACTERISATION PERCEPTIVE DES VARIETES HYBRIDES CHINOISES DU MAÏS : LA SÉLECTIVITÉ SENSORIELLE EST-ELLE DÉTERMINANTE AU BÉNIN ?

Agricultural researches usually advocate high yielding competitive crop varieties in order to supply foodstuff to the increasing population. However, this is not to care for the social dimension of adoption in the technology transfer process. That is why the present paper acknowledges actors like producers, food processors, marketers and others, whose perception with respect to growing, harvesting and processing stages of maize, to be included in the perceptive evaluation of Chinese hybrid varieties at the research centers. Four new varieties of maize are promoted: T2 (Guidan 162), T3 (Jinguyuan 688), T4 (Jinyu No.8) and T5 (Xianyu 335). Actors compare new Chinese varieties of maize to their traditional ones. On the basis of a comparative appraisal index (CAI), ie. a new variety is likely to be adopted if the differences of score between its descriptors and those of the traditional variety are greater than zero. In terms of results, T2 and T4 are the most likely to be adopted in the South and the Center. In the North, on the contrary, T5 is substituted to T4. Because of a low performance on various descriptors, T3 is unlikely to be adopted. While in the south and the center of Benin, sensorial descriptors remain decisive in the adoption profile, agromorphological and harvest stage descriptors are more likely to affect adoption in the North. Based on the increasing economic importance of maize, actors’ perception in the North significantly matters in the process of adoption of new varieties

Propagation on networks: an exact alternative perspective

By generating the specifics of a network structure only when needed (on-the-fly), we derive a simple stochastic process that exactly models the time evolution of susceptible-infectious dynamics on finite-size networks. The small number of dynamical variables of this birth-death Markov process greatly simplifies analytical calculations. We show how a dual analytical description, treating large scale epidemics with a Gaussian approximations and small outbreaks with a branching process, provides an accurate approximation of the distribution even for rather small networks. The approach also offers important computational advantages and generalizes to a vast class of systems.Comment: 8 pages, 4 figure