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Artykuły w czasopismach na temat "Réseaux bipartis"
Foureault, Fabien, Lena Ajdacic i Felix Bühlmann. "L’organisation collective du grand patronat américain". Revue française de sociologie Vol. 64, nr 1 (22.01.2024): 183–217. http://dx.doi.org/10.3917/rfs.641.0183.
Pełny tekst źródłaLaroche, Martin, i Steve Plante. "Le réseau d’acteurs et ses représentations sociales : Méthode d’évaluation de la gestion des urgences et des risques à Saint-André de Kamouraska". Canadian Journal of Emergency Management 2, nr 1 (1.01.2022). http://dx.doi.org/10.25071/e5wqez30.
Pełny tekst źródłaRozprawy doktorskie na temat "Réseaux bipartis"
Aïder, Méziane. "Réseaux d'interconnexion bipartis : colorations généralisées dans les graphes". Phd thesis, Grenoble 1, 1987. http://tel.archives-ouvertes.fr/tel-00325779.
Pełny tekst źródłaTackx, Raphaël. "Analyse de la structure communautaire des réseaux bipartis". Electronic Thesis or Diss., Sorbonne université, 2018. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2018SORUS550.pdf.
Pełny tekst źródłaIn the real world, numerous networks appear naturally, they are everywhere, in many disciplines, for example in computer science with router networks, satellite networks, webpage networks, in biology with neural networks, in ecology with biological interaction networks, in linguistic with synonym networks, in law with legal decision networks, in economy with interbank networks, in social sciences and humanities with social networks. Generally, a network reflects the interactions between many entities of a system. These interactions have different sources, a social link or a friendship link in a social network, a cable in a router network, a chemical reaction in a protein-protein interaction network, a hyperlink in a webpage network. Furthermore, the rapid democratization of digital technology in our societies, with internet in particular, leads to create new systems which can be seen as networks. Finally, all these networks depict very specific features : they come from pratical contexts, most of the time they are big (they may be comprised of several billion of nodes and links, containing a large amount of information), they share statistical properties. In this regard, they are called real-world networks or complex networks. Nowaday, network science is a research area in its own right focusing on describing and modeling these networks in order to reveal their main features and improve our understanding of their mecanisms. Most of the works in this area use graphs formalism which provides a set of mathematical tools well suited for analyzing the topology of these networks. It exists many applications, for instance applications in spread of epidemy or computer viruses, weakness of networks in case of a breakdown, attack resilience, study for link prediction, recommandation, etc. One of the major issue is the identification of community structure. The large majority of real-world networks depicts several levels of organization in their structure. Because of there is a weak global density coupled with a strong local density, we observe that nodes are usually organized into groups, called communities, which are more internally connected than they are to the rest of the network. Moreover, these structures have a meaning in the network itself, for example communities of a social network may correspond to social groups (friends, families, etc.), communities of a protein-protein network may translate fonctions of a cell, communities may be also related to similar subjects in a webpage network [...]
Chakroun, Nasr Ali. "Problèmes de circuits, chemins et diamètres dans les graphes : routage dans les réseaux". Paris 11, 1986. http://www.theses.fr/1986PA112354.
Pełny tekst źródłaBenchettara, Nasserine. "Prévision de nouveaux liens dans les réseaux d'interactions bipartis : Application au calcul de recommandation". Paris 13, 2011. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2011_benchettara.pdf.
Pełny tekst źródłaIn this work, we handle the problem of new link prediction in dynamic complex networks. We mainly focus on studying networks having a bipartite underlaying structure. We propose to apply a propositionnalization approach where each couple of nodes in the network is described by a set of topological measures. One first contribution in this thesis is to consider measures computed in the bipartite graph and also in the associated projected graphs. A supervised machine learning approach is applied. This approach though it gives some good results, suffers from the obvious problem of class skewness. We hence focus on handling this problem. Informed sub-sampling approaches are first proposed. A semi-supervised machine learning approach is also applied. All proposed approaches are applied and evaluated on real datasets used in real application of academic collaboration recommendation and product recommendation in an e-commerce site
Dumas, Maxime. "AlertWheel visualisation radiale de graphes bipartis appliquée aux systèmes de détection d'intrusions sur des réseaux informatiques". Mémoire, École de technologie supérieure, 2011. http://espace.etsmtl.ca/959/1/DUMAS_Maxime.pdf.
Pełny tekst źródłaHujsa, Thomas. "Contribution à l'étude des réseaux de Petri généralisés". Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066342/document.
Pełny tekst źródłaMany real systems and applications, including flexible manufacturing systems and embedded systems, are composed of communicating tasks and may be modeled by weighted Petri nets. The behavior of these systems can be checked on their model early on at the design phase, thus avoiding costly simulations on the designed systems. Usually, the models should exhibit three basic properties: liveness, boundedness and reversibility.Liveness preserves the possibility of executing every task, while boundedness ensures that the operations can be performed with a bounded amount ofresources. Reversibility avoids a costly initialization phase and allows resets of the system.Most existing methods to analyse these properties have exponential time complexity.By focusing on several expressive subclasses of weighted Petri nets, namely Fork-Attribution, Choice-Free, Join-Free and Equal-Conflict nets,the first polynomial algorithms that ensure liveness, boundednessand reversibility for these classes have been developed in this thesis.First, we provide several polynomial time transformations that preserve structural andbehavioral properties of weighted Petri nets, while simplifying the study of their behavior.Second, we use these transformations to obtain several polynomial sufficient conditions of livenessfor the subclasses considered. Finally, the transformations also prove useful for the study of the reversibility propertyunder the liveness assumption. We provide several characterizations and polynomial sufficient conditionsof reversibility for the same subclasses. All our conditions are scalable and can be easily implemented in real systems
Donier-Meroz, Etienne. "Graphon estimation in bipartite networks". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG010.
Pełny tekst źródłaMany real-world datasets can be represented as matrices where the entries represent interactions between two entities of different natures. These matrices are commonly known as adjacency matrices of bipartite graphs. In our work, we make the assumption that these interactions are determined by unobservable latent variables.Firstly, our main objective is to estimate the conditional expectation of the data matrix given the unobservable variables under the assumption that matrix entries are i.i.d. This estimation problem can be framed as estimating a bivariate function known as a graphon. In our study, we focus on two cases: piecewise constant graphons and Hölder-continuous graphons.We derive finite sample risk bounds for the least squares estimator. Additionally, we propose an adaptation of Lloyd's algorithm to compute an approximation this estimator and provide results from numerical experiments to evaluate the performance of these methods.Secondly, we address the limitations of the previous framework, which may not be suitable for modeling situations with bounded degrees of vertices, among other scenarios. Therefore, we extend our study to the relaxed independence assumption, where only the rows of the adjacency matrix are assumed to be independent. In this context, we specifically focus on piecewise constant graphons
Hujsa, Thomas. "Contribution à l'étude des réseaux de Petri généralisés". Electronic Thesis or Diss., Paris 6, 2014. http://www.theses.fr/2014PA066342.
Pełny tekst źródłaMany real systems and applications, including flexible manufacturing systems and embedded systems, are composed of communicating tasks and may be modeled by weighted Petri nets. The behavior of these systems can be checked on their model early on at the design phase, thus avoiding costly simulations on the designed systems. Usually, the models should exhibit three basic properties: liveness, boundedness and reversibility.Liveness preserves the possibility of executing every task, while boundedness ensures that the operations can be performed with a bounded amount ofresources. Reversibility avoids a costly initialization phase and allows resets of the system.Most existing methods to analyse these properties have exponential time complexity.By focusing on several expressive subclasses of weighted Petri nets, namely Fork-Attribution, Choice-Free, Join-Free and Equal-Conflict nets,the first polynomial algorithms that ensure liveness, boundednessand reversibility for these classes have been developed in this thesis.First, we provide several polynomial time transformations that preserve structural andbehavioral properties of weighted Petri nets, while simplifying the study of their behavior.Second, we use these transformations to obtain several polynomial sufficient conditions of livenessfor the subclasses considered. Finally, the transformations also prove useful for the study of the reversibility propertyunder the liveness assumption. We provide several characterizations and polynomial sufficient conditionsof reversibility for the same subclasses. All our conditions are scalable and can be easily implemented in real systems
Koptelov, Maksim. "Link prediction in bipartite multi-layer networks, with an application to drug-target interaction prediction". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC211.
Pełny tekst źródłaMany aspects from real life with bi-relational structure can be modeled as bipartite networks. This modeling allows the use of some standard solutions for prediction and/or recommendation of new relations between these objects in such networks. Known as the link prediction task, it is a widely studied problem in network science for single graphs, networks assuming one type of interaction between vertices. For multi-layer networks, allowing more than one type of edges between vertices, the problem is not yet fully solved.The motivation of this thesis comes from the importance of an application task, drug-target interaction prediction. Searching valid drug candidates for a given biological target is an essential part of modern drug development. In this thesis, the problem is modeled as link prediction in a bipartite multi-layer network. Modeling the problem in this setting helps to aggregate different sources of information into one single structure and as a result to improve the quality of link prediction.The thesis mostly focuses on the problem of link prediction in bipartite multi-layer networks and makes two main contributions on this topic.The first contribution provides a solution for solving link prediction in the given setting without limiting the number and type of networks, the main constrains of the state of the art methods. Modeling random walk in the fashion of PageRank, the algorithm that we developed is able to predict new interactions in the network constructed from different sources of information. The second contribution, which solves link prediction using community information, is less straight-forward and more dependent on fixing the parameters, but provides better results. Adopting existing community measures for link prediction to the case of bipartite multi-layer networks and proposing alternative ways for exploiting communities, the method offers better performance and efficiency. Additional evaluation on the data of a different origin than drug-target interactions demonstrate the genericness of proposed approach.In addition to the developed approaches, we propose a framework for validation of predicted interactions founded on an external resource. Based on a collection of biomedical concepts used as a knowledge source, the framework is able to perform validation of drug-target pairs using proposed confidence scores. An evaluation of predicted interactions performed on unseen data shows effectiveness of this framework.At the end, a problem of identification and characterization of promiscuous compounds existing in the drug development process is discussed. The problem is solved as a machine learning classification task. The contribution includes graph mining and sampling approaches. In addition, a graphical interface was developed to provide feedback of the result for experts
Topart, Hélène. "Etude d’une nouvelle classe de graphes : les graphes hypotriangulés". Thesis, Paris, CNAM, 2011. http://www.theses.fr/2011CNAM0776/document.
Pełny tekst źródłaIn this thesis, we define a new class of graphs : the hypochordal graphs. These graphs satisfy that for any path of length two, there exists a chord or another path of length two between its two endpoints. This class can represent robust networks. Indeed, we show that in such graphs, in the case of an edge or a vertex deletion, the distance beween any pair of nonadjacent vertices remains unchanged. Then, we study several properties for this class of graphs. Especially, after introducing a family of specific partitions, we show the relations between some of these partitions and hypochordality. Moreover, thanks to these partitions, we characterise minimum hypochordal graph, that are, among connected hypochordal graphs, those that minimise the number of edges for a given number of vertices. In a second part, we study the complexity, for hypochordal graphs, of problems that are NP-hard in the general case. We first show that the classical problems of hamiltonian cycle, colouring, maximum clique and maximum stable set remain NP-hard for this class of graphs. Then, we analyse graph modification problems : deciding the minimal number of edges to add or delete from a graph, in order to obtain an hypochordal graph. We study the complexity of these problems for sevaral classes of graphs