Un algorithme Hongrois pour l'appariement de graphes avec correction d'erreurs

International audienceBipartite graph matching algorithms become more and more popular to solve error-correcting graph matching problems and to approximate the graph edit distance of two graphs. However, the memory requirements and execution times of this method are respectively proportional to (n + m) 2 and (n + m) 3 where n and m are the order of the graphs. Subsequent developments reduced these complexities. However , these improvements are valid only under some constraints on the parameters of the graph edit distance. We propose in this paper a new formulation of the bipartite graph matching algorithm designed to solve efficiently the associated graph edit distance problem. The resulting algorithm requires O(nm) memory space and O(min(n, m) 2 max(n, m)) execution times.L'appariement de graphes biparti deviennent de plus en plus populaires pour résoudre des problèmes d'appariement de graphes avec correction d'erreurs et pour approximer la distance d'édition sur graphes. Cependant, les exigences en mémoire et temps de calcul de cette méthode sont respectivement proportionnels à (n + m)^2 et (n + m)^3 où n et m représentent la taille des deux graphes. Des développements ultérieurs ont réduit ces complexités. Cependant, ces améliorations ne sont valables que sous certaines contraintes sur les paramètres de la distance d'édition. Nous proposons dans cet article une nouvelle formulation de l'algorithme Hongrois conçu pour résoudre efficacement le problème de distance d'édition associé. L'algorithme résultat nécessite un espace mémoire O (nm) et des temps d'exécution O (min (n, m)^2 max (n, m))

Maximal Independent Sets for Pooling in Graph Neural Networks

Convolutional Neural Networks (CNNs) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete lattice into a reduced lattice with the same connectivity and allows reduction functions to consider all pixels in an image. However, there is no pooling that satisfies these properties for graphs. In fact, traditional graph pooling methods suffer from at least one of the following drawbacks: Graph disconnection or overconnection, low decimation ratio, and deletion of large parts of graphs. In this paper, we present three pooling methods based on the notion of maximal independent sets that avoid these pitfalls. Our experimental results confirm the relevance of maximal independent set constraints for graph pooling

Maximal Independent Vertex Set applied to Graph Pooling

Convolutional neural networks (CNN) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete grid into a reduced grid with the same connectivity and allows reduction functions to take into account all the pixels of an image. However, a pooling satisfying such properties does not exist for graphs. Indeed, some methods are based on a vertex selection step which induces an important loss of information. Other methods learn a fuzzy clustering of vertex sets which induces almost complete reduced graphs. We propose to overcome both problems using a new pooling method, named MIVSPool. This method is based on a selection of vertices called surviving vertices using a Maximal Independent Vertex Set (MIVS) and an assignment of the remaining vertices to the survivors. Consequently, our method does not discard any vertex information nor artificially increase the density of the graph. Experimental results show an increase in accuracy for graph classification on various standard datasets

Graph médian généralisé via des minimisations alternées.

International audienceComputing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach.Calculer un graphe prototype peut constituer une étape centrale pour des méthodes de clustering ou de classification. Toutefois, ce calcul est NP-difficile même pour des classes de graphes simples. Nous proposons dans ce papier une approche efficace basée sur une minimisation alternée pour calculer le graphe médian d'un ensemble. Cette approche s'appuie sur une définition claire du processus d'optimisation et inclue l'étiquetage à la fois des nœuds et des arêtes. Ce processus itératif optimise les opérations à effectuer alternativement sur les sommets et les arêtes. Plusieurs expériences sur des jeux de données différents montrent l'efficacité de notre approche

Le concept de maladies virales émergentes : quel risque de zoonose pour La Réunion ?

A La Réunion, le risque d'émergence de maladies virales est constitué par plusieurs zoonoses virales qu'il convient de surveiller: infections à virus Sindbis, virus de l'encéphalite japonaise, virus Wesselsbron, virus Nipah, virus Zika, virus West Nile, virus de la fièvre de la vallée du Rift. La lutte contre ces maladies virales émergentes (MVE) passe par une détection précoce des cas et donc un système de surveillance doté d'un véritable réseau d'information, d'alerte et de prévention international. (Résumé d'auteur

Implicit and Explicit Graph Embedding: Comparison of both Approaches on Chemoinformatics Applications

International audienceDefining similarities or distances between graphs is one of the bases of the structural pattern recognition field. An important trend within this field consists in going beyond the simple formulation of simi- larity measures by studying properties of graph's spaces induced by such distance or similarity measures . Such a problematic is closely related to the graph embedding problem. In this article, we investigate two types of similarity measures. The first one is based on the notion of graph edit distance which aims to catch a global dissimilarity between graphs. The second family is based on comparisons of bags of patterns extracted from graphs to be compared. Both approaches are detailed and their performances are evaluated on different chemoinformatics problems

Важлива складова національної безпеки (Проблеми захисту науково-технічної інформації)

У статті порушується проблема забезпечення захисту інформаційних ресурсів у науково- технічній сфері. Обґрунтовується значення науково-технологічного потенціалу для економічного і соціального розвитку України. Доводиться необхідність ґрунтовної розробки відповідної нормативно-правової бази.The article is dedicated to the problem of ensuring of protection of information resources in scientific-technical sphere, significance of the scientific-technological potential for economical and social growth of Ukraine is grounded. Necessity of well-founded development of correspondent normative and legal base is proved

Guillain-Barré Syndrome after Chikungunya Infection

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