Дисертації з теми "Structural Graph Representations"
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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.
Повний текст джерелаPattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyze patterns. Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding. In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies.
Sadeghi, Kayvan. "Graphical representation of independence structures." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:86ff6155-a6b9-48f9-9dac-1ab791748072.
Повний текст джерелаTsitsulin, Anton [Verfasser]. "Similarities and Representations of Graph Structures / Anton Tsitsulin." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1238687229/34.
Повний текст джерелаGurung, Topraj. "Compact connectivity representation for triangle meshes." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47709.
Повний текст джерелаLee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.
Повний текст джерелаBandyopadhyay, Bortik. "Querying Structured Data via Informative Representations." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595447189545086.
Повний текст джерелаGkirtzou, Aikaterini. "Sparsity regularization and graph-based representation in medical imaging." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00960163.
Повний текст джерелаPeng, Chong. "Integrating Feature and Graph Learning with Factorization Models for Low-Rank Data Representation." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1464.
Повний текст джерелаKim, Pilho. "E-model event-based graph data model theory and implementation /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29608.
Повний текст джерелаCommittee Chair: Madisetti, Vijay; Committee Member: Jayant, Nikil; Committee Member: Lee, Chin-Hui; Committee Member: Ramachandran, Umakishore; Committee Member: Yalamanchili, Sudhakar. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Soares, Telma Woerle de Lima. "Estruturas de dados eficientes para algoritmos evolutivos aplicados a projeto de redes." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28052009-163303/.
Повний текст джерелаNetwork design problems (NDPs) are very important since they involve several applications from areas of Engineering and Sciences. In order to solve the limitations of traditional algorithms for NDPs that involve real world complex networks (in general, modeled by large-scale complete or sparse graphs), heuristics, such as evolutionary algorithms (EAs), have been investigated. Recent researches have shown that appropriate data structures can improve EA performance when applied to NDPs. One of these data structures is the Node-depth Encoding (NDE). In general, the performance of EAs with NDE has presented relevant results for large-scale NDPs. This thesis investigates the development of a new representation, based on NDE, called Node-depth-degree Encoding (NDDE). The NDDE is composed for improvements of the NDE operators and the development of new reproduction operators that enable the recombination of solutions. In this way, we developed a recombination operator to work with both non-complete and complete graphs, called EHR (Evolutionary History Recombination Operator). We also developed two other operators to work only with complete graphs, named NOX and NPBX. These improvements have the advantage of retaining the computational complexity of the operators relatively low in order to improve the EA performance. The analysis of representation properties have shown that NDDE is a redundant representation and, for this reason, we proposed some strategies to avoid it. This analysis also showed that EHR has low running time and it does not have bias, moreover, it revealed that NOX and NPBX have bias to trees like stars. The application of an EA using the NDDE to classic NDPs, such as, optimal communication spanning tree, degree-constraint minimum spanning tree and one-max tree, showed that the larger the instance is, the better the performance will be in comparison whit other EAs applied to NDPs in the literatura. An EA using the NDE with EHR was applied to a real-world NDP of reconfiguration of energy distribution systems. The results showed that EHR significantly decrease the convergence time of the EA
Zhu, Xueyun. "Vlist and Ering: compact data structures for simplicial 2-complexes." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50389.
Повний текст джерелаEricson, Petter. "Complexity and expressiveness for formal structures in Natural Language Processing." Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-135014.
Повний текст джерелаKheirbek, Ammar. "Modèle d'intégration d'un système de recherche d'informations et d'un système hypermédia basé sur le formalisme des graphes conceptuels." Université Joseph Fourier (Grenoble), 1995. http://www.theses.fr/1995GRE10045.
Повний текст джерела"Three-dimensional knowledge representation using extended structure graph grammars." Thesis, 2014. http://hdl.handle.net/10210/12970.
Повний текст джерелаThe purpose of this disssertation is to study methods to represent structures in three-dimensions. Due to the fact that chemical molecules are mostly complex three-dimensional structures, we used chemical molecules as our application domain. A literature study of current chemical information systems was undertaken. The whole spectrum of information systems was covered because almost all of these systems represent chemical molecules in one way or another. Various methods of three-dimensional structure representation were found in our literature study. All of these methods were discussed in the context of its own application domain. Structure graph grammars were examined and explained in detail. A small object-based system with structure graph grammars as the underlying principle was developed. We speculated on the use of such "intelligent" graph grammars in structure interpretation and identification. Further research in this area was also identified.
Vittadello, Sean T. "The representation theory of numerical semigroups and the ideal structure of Exel's crossed product." Thesis, 2008. http://hdl.handle.net/1959.13/1418768.
Повний текст джерелаWe study representations of numerical semigroups Σ by isometries on Hilbert space with commuting range projections. Our main theorem says that each such representation is unitarily equivalent to the direct sum of a representation by unitaries and a finite number of multiples of particular concrete representations by isometries. We use our main theorem to identify the faithful representations of the C*-algebra C*(Σ) generated by a universal isometric representation with commuting range projections, and also prove a structure theorem for C*(Σ). We also investigate the ideal structure of Exel's crossed product C₀(T)⋊α,Lℕ. We give conditions describing precisely when C₀(T)⋊α,Lℕ is simple. We provide a complete description of the gauge-invariant ideals of C₀(T)⋊α,Lℕ, and give a condition which ensures that every ideal of C₀(T)⋊α,Lℕ is gauge invariant. Under the assumption that Τ is second countable, we describe the primitive ideal structure of C₀(T)⋊α,Lℕ.
(8086652), Guilherme Maia Rodrigues Gomes. "Hypothesis testing and community detection on networks with missingness and block structure." Thesis, 2019.
Знайти повний текст джерелаFerbarová, Gabriela. "Konceptuální struktury jako nástroj reprezentace znalost." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-351883.
Повний текст джерелаHahmann, Torsten. "Model-Theoretic Analysis of Asher and Vieu's Mereotopology." Thesis, 2008. http://hdl.handle.net/1807/10432.
Повний текст джерелаTshilombo, Mukinayi Hermenegilde. "Cohomologies on sympletic quotients of locally Euclidean Frolicher spaces." Thesis, 2015. http://hdl.handle.net/10500/19942.
Повний текст джерелаMathematical Sciences
D. Phil. (Mathematics)