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Tesi sul tema "Embedding de graph"

Autore: Grafiati

Pubblicato: 22 febbraio 2025

Ultima modifica: 31 luglio 2025

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1

Zhang, Zheng. "Explorations in Word Embeddings : graph-based word embedding learning and cross-lingual contextual word embedding learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS369/document.

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Les plongements lexicaux sont un composant standard des architectures modernes de traitement automatique des langues (TAL). Chaque fois qu'une avancée est obtenue dans l'apprentissage de plongements lexicaux, la grande majorité des tâches de traitement automatique des langues, telles que l'étiquetage morphosyntaxique, la reconnaissance d'entités nommées, la recherche de réponses à des questions, ou l'inférence textuelle, peuvent en bénéficier. Ce travail explore la question de l'amélioration de la qualité de plongements lexicaux monolingues appris par des modèles prédictifs et celle de la mise

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2

Ahmed, Algabli Shaima. "Learning the Graph Edit Distance through embedding the graph matching." Doctoral thesis, Universitat Rovira i Virgili, 2020. http://hdl.handle.net/10803/669612.

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Els gràfics són estructures de dades abstractes que s’utilitzen per modelar problemes reals amb dues entitats bàsiques: nodes i vores. Cada node o vèrtex representa un punt d'interès rellevant d'un problema i cada vora representa la relació entre aquests punts. Es poden atribuir nodes i vores per augmentar la precisió del model, cosa que significa que aquests atributs podrien variar des de vectors de característiques fins a etiquetes de descripció. A causa d'aquesta versatilitat, s'han trobat moltes aplicacions en camps com la visió per ordinador, la biomèdica i l'anàlisi de xarxa, etc., l

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3

Carroll, Douglas Edmonds. "Embedding parameterized graph classes into normed spaces." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1324389171&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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4

Rocha, Mário. "The embedding of complete bipartite graphs onto grids with a minimum grid cutwidth." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2311.

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5

Dube, Matthew P. "An Embedding Graph for 9-Intersection Topological Spatial Relations." Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/DubeMP2009.pdf.

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6

MONDAL, DEBAJYOTI. "Embedding a Planar Graph on a Given Point Set." Springer-Verlag Berlin, 2012. http://hdl.handle.net/1993/8869.

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A point-set embedding of a planar graph G with n vertices on a set S of n points is a planar straight-line drawing of G, where each vertex of G is mapped to a distinct point of S. We prove that the point-set embeddability problem is NP-complete for 3-connected planar graphs, answering a question of Cabello [20]. We give an O(nlog^3n)-time algorithm for testing point-set embeddability of plane 3-trees, improving the algorithm of Moosa and Rahman [60]. We prove that no set of 24 points can support all planar 3-trees with 24 vertices, partially answering a question of Kobourov [55]. We compute 2-

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7

Mitropolitsky, Milko. "On the Impact of Graph Embedding on Device Placement." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280435.

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Modern neural network (NN) models require more data and parameters in or- der to perform ever more complex tasks. When an NN model becomes too massive to fit on a single machine, it may need to be distributed across multi- ple machines. What policies should be used when distributing an NN model, and more concretely how different parts of the model should be disseminated across the various machines is called the device placement problem. Tackling the matter is the focus of this thesis.Previous approaches have required the placement policies to be created manually by human experts. Since that me

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8

Behzadi, Lila. "An improved spring-based graph embedding algorithm and LayoutShow, a Java environment for graph drawing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq43368.pdf.

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9

Tahraoui, Mohammed Amin. "Coloring, packing and embedding of graphs." Phd thesis, Université Claude Bernard - Lyon I, 2012. http://tel.archives-ouvertes.fr/tel-00995041.

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In this thesis, we investigate some problems in graph theory, namelythe graph coloring problem, the graph packing problem and tree pattern matchingfor XML query processing. The common point between these problems is that theyuse labeled graphs.In the first part, we study a new coloring parameter of graphs called the gapvertex-distinguishing edge coloring. It consists in an edge-coloring of a graph G whichinduces a vertex distinguishing labeling of G such that the label of each vertex isgiven by the difference between the highest and the lowest colors of its adjacentedges. The minimum number of

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10

Okuno, Akifumi. "Studies on Neural Network-Based Graph Embedding and Its Extensions." Kyoto University, 2020. http://hdl.handle.net/2433/259075.

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11

Gibert, Domingo Jaume. "Vector Space Embedding of Graphs via Statistics of Labelling Information." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/96240.

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El reconeixement de patrons és la tasca que pretén distingir objectes entre diferents classes. Quan aquesta tasca es vol solucionar de forma automàtica un pas crucial és el com representar formalment els patrons a l'ordinador. En funció d'aquests formalismes, podem distingir entre el reconeixement estadístic i l'estructural. El primer descriu objectes com un conjunt de mesures col·locats en forma del que s'anomena un vector de característiques. El segon assumeix que hi ha relacions entre parts dels objectes que han de quedar explícitament representades i per tant fa servir estructures relacio

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12

Wåhlin, Lova. "Towards Machine Learning Enabled Automatic Design of IT-Network Architectures." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249213.

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There are many machine learning techniques that cannot be performed on graph-data. Techniques such as graph embedding, i.e mapping a graph to a vector, can open up a variety of machine learning solutions. This thesis addresses to what extent static graph embedding techniques can capture important characteristics of an IT-architecture graph, with the purpose of embedding the graphs in a common euclidean vector space that can serve as the state space in a reinforcement learning setup. The metric used for evaluating the performance of the embedding is the security of the graph, i.e the time it wo

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13

PALUMBO, ENRICO. "Knowledge Graph Embeddings for Recommender Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850588.

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14

Boyer, John M. "Simplified O(n) algorithms for planar graph embedding, Kuratowski subgraph isolation, and related problems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ62507.pdf.

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15

Chen, Xiaofeng. "Plane Permutations and their Applications to Graph Embeddings and Genome Rearrangements." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77535.

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Maps have been extensively studied and are important in many research fields. A map is a 2-cell embedding of a graph on an orientable surface. Motivated by a new way to read the information provided by the skeleton of a map, we introduce new objects called plane permutations. Plane permutations not only provide new insight into enumeration of maps and related graph embedding problems, but they also provide a powerful framework to study less related genome rearrangement problems. As results, we refine and extend several existing results on enumeration of maps by counting plane permutations filt

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16

Wappler, Markus. "On Graph Embeddings and a new Minor Monotone Graph Parameter associated with the Algebraic Connectivity of a Graph." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-115518.

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Abstract (sommario):

We consider the problem of maximizing the second smallest eigenvalue of the weighted Laplacian of a (simple) graph over all nonnegative edge weightings with bounded total weight. We generalize this problem by introducing node significances and edge lengths. We give a formulation of this generalized problem as a semidefinite program. The dual program can be equivalently written as embedding problem. This is fifinding an embedding of the n nodes of the graph in n-space so that their barycenter is at the origin, the distance between adjacent nodes is bounded by the respective edge length, and the

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17

Luqman, Muhammad Muzzamil. "Fuzzy multilevel graph embedding for recognition, indexing and retrieval of graphic document images." Thesis, Tours, 2012. http://www.theses.fr/2012TOUR4005/document.

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Cette thèse aborde le problème du manque de performance des outils exploitant des représentationsà base de graphes en reconnaissance des formes. Nous proposons de contribuer aux nouvellesméthodes proposant de tirer partie, à la fois, de la richesse des méthodes structurelles et de la rapidité des méthodes de reconnaissance de formes statistiques. Deux principales contributions sontprésentées dans ce manuscrit. La première correspond à la proposition d'une nouvelle méthode deprojection explicite de graphes procédant par analyse multi-facettes des graphes. Cette méthodeeffectue une caractérisati

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18

Siameh, Theophilus. "Graph Analytics Methods In Feature Engineering." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3307.

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High-dimensional data sets can be difficult to visualize and analyze, while data in low-dimensional space tend to be more accessible. In order to aid visualization of the underlying structure of a dataset, the dimension of the dataset is reduced. The simplest approach to accomplish this task of dimensionality reduction is by a random projection of the data. Even though this approach allows some degree of visualization of the underlying structure, it is possible to lose more interesting underlying structure within the data. In order to address this concern, various supervised and unsupervised l

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19

Zhu, Xiaoting. "Systematic Assessment of Structural Features-Based Graph Embedding Methods with Application to Biomedical Networks." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592394966493963.

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20

Shanmugam, Sakthivadivel Saravanakumar. "Fast-NetMF: Graph Embedding Generation on Single GPU and Multi-core CPUs with NetMF." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1557162076041442.

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21

Frimodig, Matilda, and Sivertsson Tom Lanhed. "A Comparative study of Knowledge Graph Embedding Models for use in Fake News Detection." Thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43228.

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During the past few years online misinformation, generally referred to as fake news, has been identified as an increasingly dangerous threat. As the spread of misinformation online has increased, fake news detection has become an active line of research. One approach is to use knowledge graphs for the purpose of automated fake news detection. While large scale knowledge graphs are openly available these are rarely up to date, often missing the relevant information needed for the task of fake news detection. Creating new knowledge graphs from online sources is one way to obtain the missing info

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22

Fowler, Joe. "Unlabled Level Planarity." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/195812.

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Consider a graph G with vertex set V in which each of the n vertices is assigned a number from the set {1, ..., k} for some positive integer k. This assignment phi is a labeling if all k numbers are used. If phi does not assign adjacent vertices the same label, then phi partitions V into k levels. In a level drawing, the y-coordinate of each vertex matches its label and the edges are drawn strictly y-monotone. This leads to level drawings in the xy-plane where all vertices with label j lie along the line lj = {(x, j) : x in Reals} and where each edge crosses any of the k horizontal lines lj

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23

Reiß, Susanna. "Optimizing Extremal Eigenvalues of Weighted Graph Laplacians and Associated Graph Realizations." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-93599.

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This thesis deals with optimizing extremal eigenvalues of weighted graph Laplacian matrices. In general, the Laplacian matrix of a (weighted) graph is of particular importance in spectral graph theory and combinatorial optimization (e.g., graph partition like max-cut and graph bipartition). Especially the pioneering work of M. Fiedler investigates extremal eigenvalues of weighted graph Laplacians and provides close connections to the node- and edge-connectivity of a graph. Motivated by Fiedler, Göring et al. were interested in further connections between structural properties of the graph and

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24

Sun, Jiankai. "Directed Graph Analysis: Algorithms and Applications." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1565797455907422.

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25

Damay, Gabriel. "Dynamic Decision Trees and Community-based Graph Embeddings : towards Interpretable Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT047.

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L'apprentissage automatique est le domaine des sciences informatiques dont le but est de créer des modèles et des solutions à partir de données sans savoir exactement les instructions qui dirigent intrinsèquement ces modèles. Ce domaine a obtenu des résultats impressionnants mais il est l'objet le sujet d'inquiétudes en raison notamment de l'impossibilité de comprendre et d'auditer les modèles qu'il produit. L'apprentissage automatique interprétable propose une solution à ces inquiétudes en créant des modèles qui sont interprétables de façon inhérante. Cette thèse contribue à l'apprentissage a

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26

Bläsius, Thomas [Verfasser], and D. [Akademischer Betreuer] Wagner. "New Approaches to Classic Graph-Embedding Problems - Orthogonal Drawings & Constrained Planarity / Thomas Bläsius. Betreuer: D. Wagner." Karlsruhe : KIT-Bibliothek, 2015. http://d-nb.info/1075809401/34.

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27

Kilinc, Ismail Ozsel. "Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.

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Abstract (sommario):

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and uns

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28

Holmström, Oskar. "Exploring Transformer-Based Contextual Knowledge Graph Embeddings : How the Design of the Attention Mask and the Input Structure Affect Learning in Transformer Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175400.

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The availability and use of knowledge graphs have become commonplace as a compact storage of information and for lookup of facts. However, the discrete representation makes the knowledge graph unavailable for tasks that need a continuous representation, such as predicting relationships between entities, where the most probable relationship needs to be found. The need for a continuous representation has spurred the development of knowledge graph embeddings. The idea is to position the entities of the graph relative to each other in a continuous low-dimensional vector space, so that their relati

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29

Boschin, Armand. "Machine learning techniques for automatic knowledge graph completion." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT016.

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Un graphe de connaissances est un graphe orienté dont les nœuds sont des entités et les arêtes, typées par une relation, représentent des faits connus liant les entités. Ces graphes sont capables d'encoder une grande variété d'information mais leur construction et leur exploitation peut se révéler complexe. Historiquement, des méthodes symboliques ont permis d'extraire des règles d'interaction entre entités et relations, afin de corriger des anomalies ou de prédire des faits manquants. Plus récemment, des méthodes d'apprentissage de représentations vectorielles, ou plongements, ont tenté de ré

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30

Fang, Chunsheng. "Novel Frameworks for Mining Heterogeneous and Dynamic Networks." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.

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31

Prouteau, Thibault. "Graphs,Words, and Communities : converging paths to interpretability with a frugal embedding framework." Electronic Thesis or Diss., Le Mans, 2024. http://www.theses.fr/2024LEMA1006.

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L'apprentissage de représentations au travers des méthodes de plongements de mots (word embedding) et de graphes (graph embedding) permet des représentations distribuées de l'information. Ces représentations peuvent à leur tour être utilisées en entrée d'algorithmes d'apprentissage automatique. Au cours des deux dernières décennies, les tâches de plongement de nœuds et de mots sont passées d'approches par factorisation matricielle qui pouvaient être réalisées en quelques minutes à de grands modèles nécessitant des quantités toujours plus importantes de données d’apprentissage et parfois des se

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32

Helmberg, Christoph, Israel Rocha, and Uwe Schwerdtfeger. "A Combinatorial Algorithm for Minimizing the Maximum Laplacian Eigenvalue of Weighted Bipartite Graphs." Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-175057.

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We give a strongly polynomial time combinatorial algorithm to minimise the largest eigenvalue of the weighted Laplacian of a bipartite graph. This is accomplished by solving the dual graph embedding problem which arises from a semidefinite programming formulation. In particular, the problem for trees can be solved in time cubic in the number of vertices.

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33

Lee, Zed Heeje. "A graph representation of event intervals for efficient clustering and classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281947.

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Sequences of event intervals occur in several application domains, while their inherent complexity hinders scalable solutions to tasks such as clustering and classification. In this thesis, we propose a novel spectral embedding representation of event interval sequences that relies on bipartite graphs. More concretely, each event interval sequence is represented by a bipartite graph by following three main steps: (1) creating a hash table that can quickly convert a collection of event interval sequences into a bipartite graph representation, (2) creating and regularizing a bi-adjacency matrix

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34

Liang, Jiongqian. "Human-in-the-loop Machine Learning: Algorithms and Applications." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523988406039076.

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35

Bertoni, Eleonora. "Apprendimento non supervisionato di rappresentazioni e legami di similarità tra eventi menzionati nella letteratura biomedica." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24748/.

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L'estrazione automatica degli eventi biomedici dalla letteratura scientifica ha catturato un forte interesse nel corso degli ultimi anni, dimostrandosi in grado di riconoscere interazioni complesse e semanticamente ricche espresse all'interno del testo. Purtroppo però, esistono davvero pochi lavori focalizzati sull'apprendimento di embedding o di metriche di similarità per i grafi evento. Questa lacuna lascia le relazioni biologiche scollegate, impedendo l'applicazione di tecniche di machine learning che potrebbero dare un importante contributo al progresso scientifico. Approfittando dei va

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36

Bloyet, Nicolas. "Caractérisation et plongement de sous-graphes colorés : application à la construction de modèles structures à activité (QSAR)." Thesis, Lorient, 2019. http://www.theses.fr/2019LORIS546.

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Dans le domaine de la chimie, il est intéressant de pouvoir estimer des propriétés physico- chimiques de molécules, notamment pour des applications industrielles. Celles-ci sont difficiles à estimer par simulations physique, présentant une complexité temporelle prohibitive. L'émergence des données (publiques ou privées) ouvre toutefois de nouvelles perspectives pour le traitement de ces problèmes par des méthodes statistiques et d'apprentissage automatique. La principale difficulté réside dans la caractérisation des molécules : celles-ci s'apparentent davantage à un réseau d'atomes (autrement

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37

Muller, Carole. "Minor-closed classes of graphs: Isometric embeddings, cut dominants and ball packings." Doctoral thesis, Universite Libre de Bruxelles, 2021. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/331629.

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Une classe de graphes est close par mineurs si, pour tout graphe dans la classe et tout mineur de ce graphe, le mineur est ́egalement dans la classe. Par un fameux th ́eor`eme de Robertson et Seymour, nous savons que car- act ́eriser une telle classe peut ˆetre fait `a l’aide d’un nombre fini de mineurs exclus minimaux. Ceux-ci sont des graphes qui n’appartiennent pas `a la classe et qui sont minimaux dans le sens des mineurs pour cette propri ́et ́e.Dans cette thèse, nous étudions trois problèmes à propos de classes de graphes closes par mineurs. Les deux premiers sont reliés à la caractérisa

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38

Chennupati, Nikhil. "Recommending Collaborations Using Link Prediction." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1621899961924795.

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39

Cassagnes, Cyril. "Architecture autonome et distribuée d’adressage et de routage pour la flexibilité des communications dans l’internet." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14600/document.

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Les schémas de routage locaux basés sur des coordonnées prises dans le plan hyperbolique ont attiré un intérêt croissant depuis quelques années. Cependant, les solutions proposées sont toutes appliquées à des réseaux au topologie aléatoire et au nombre de nœuds limités. Dans le même temps, plusieurs travaux se sont concentrés sur la création de modèle topologique basé sur les lois de la géométrie hyperbolique. Dans ce cas, Il est montré que les graphes ont des topologies semblables à Internet et qu'un routage local hyperbolique atteint une efficacité proche de la perfection. Cependant, ces gra

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40

Simonovsky, Martin. "Deep learning on attributed graphs." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1133/document.

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Le graphe est un concept puissant pour la représentation des relations entre des paires d'entités. Les données ayant une structure de graphes sous-jacente peuvent être trouvées dans de nombreuses disciplines, décrivant des composés chimiques, des surfaces des modèles tridimensionnels, des interactions sociales ou des bases de connaissance, pour n'en nommer que quelques-unes. L'apprentissage profond (DL) a accompli des avancées significatives dans une variété de tâches d'apprentissage automatique au cours des dernières années, particulièrement lorsque les données sont structurées sur une grille

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Sarker, Bishnu. "On Graph-Based Approaches for Protein Function Annotation and Knowledge Discovery." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0094.

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Les progrès des technologies de séquençage génomique ont conduit à une croissance exponentielle du nombre de séquences protéiques dans les bases de données publiques. Il est important d’exploiter cette énorme quantité de données pour décrire les êtres vivants au niveau moléculaire, et ainsi mieux comprendre les processus pathologiques humains et accélérer la découverte de médicaments. Une condition préalable, cependant, est que toutes ces protéines soient annotées avec des propriétés fonctionnelles telles que les numéros de commission enzymatique (EC) ou les termes de l’ontologie « Gene Ontolo

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42

Zhu, Ruifeng. "Contribution to graph-based manifold learning with application to image categorization." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA015.

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Les algorithmes d'apprentissage de représentation de données à base de graphes sont considérés comme une technique puissante pour l'extraction de caractéristiques et la réduction de dimensionnalité dans les domaines de la reconnaissance de formes, la vision par ordinateur et l'apprentissage automatique. Ces algorithmes utilisent les informations contenues dans les similitudes d’échantillons (par paire) et la matrice du graphe pondéré pour révéler la structure géométrique intrinsèque de données. Ces algorithmes sont capables de récupérer une structure de faible dimension à partir de données de

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43

Labelle, François. "Graph embeddings and approximate graph coloring." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0031/MQ64386.pdf.

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44

Islam, Md Kamrul. "Explainable link prediction in large complex graphs - application to drug repurposing." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0203.

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Abstract (sommario):

De nombreux systèmes complexes du monde réel peuvent être représentés par des graphes, où les nœuds représentent des entités et les liens des relations entre les paires de nœuds. La prédiction de liens (LP) est l'un des problèmes les plus intéressants et les plus anciens dans le domaine de l'exploration de graphes ; elle prédit la probabilité d'un lien entre deux nœuds non connectés. Cette thèse étudie le problème LP dans les graphes simples et les graphes de connaissances (KGs). La première partie de cette thèse se concentre sur le problème LP dans les graphes simples. Dans la première étude,

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45

Turner, Bethany. "Embeddings of Product Graphs Where One Factor is a Hypercube." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2455.

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Voltage graph theory can be used to describe embeddings of product graphs if one factor is a Cayley graph. We use voltage graphs to explore embeddings of various products where one factor is a hypercube, describing some minimal and symmetrical embeddings. We then define a graph product, the weak symmetric difference, and illustrate a voltage graph construction useful for obtaining an embedding of the weak symmetric difference of an arbitrary graph with a hypercube.

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46

Ngwobia, Sunday C. "Capturing Knowledge of Emerging Entities from the Extended Search Snippets." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157309507473671.

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47

Yelle, Céline. "Stack Number, Track Number, and Layered Pathwidth." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40348.

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In this thesis, we consider three parameters associated with graphs : stack number, track number, and layered pathwidth. Our first result is to show that the stack number of any graph is at most 4 times its layered pathwidth. This result complements an existing result of Dujmovic et al. that showed that the queue number of a graph is at most 3 times its layered pathwidth minus one (Dujmovic, Morin, and Wood [SIAM J. Comput., 553–579, 2005]). Our second result is to show that graphs of track number at most 3 have layered pathwidth at most 4. This answers an open question posed by Banister et al

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48

Djuphammar, Felix. "Efficient graph embeddings with community detection." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185134.

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Networks are useful when modeling interactions in real-world systems based on relational data. Since networks often contain thousands or millions of nodes and links, analyzing and exploring them requires powerful visualizations. Presenting the network nodes in a map-like fashion provides a large scale overview of the data while also providing specific details. A suite of algorithms can compute an appropriate layout of all nodes for the visualization. However, these algorithms are computationally expensive when applied to large networks because they must repeatedly derive relations between ever

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49

Hayashi, Kazuki. "Reinforcement Learning for Optimal Design of Skeletal Structures." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263614.

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50

Cooley, Oliver Josef Nikolaus. "Embedding Problems for Graphs and Hypergraphs." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/766/.

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This thesis deals with the problem of finding some substructure within a large graph or hypergraph. In the case of graphs, we consider the substructures consisting of fixed subgraphs or families of subgraphs, perfect graph packings and spanning subgraphs. In the case of hypergraphs we consider the substructure consisting of a hypergraph whose order is linear in the order of the large hypergraph. I will show how these problems are extensions of more basic and well-known results in graph theory. I will give full proofs of three new embedding results, two for graphs and one for hypergraphs. I wil

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