Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 3598 for “"graph"”.
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Graph matching by graph neural network
Graph matching or network alignment refers to the problem of matching two correlated graphs. This thesis presents a deep Q learning based method, which represents the matching process by a graph neural network. By breaking the symmetry, the parameterized graph neural network is able to capture a …
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Problems in Graph Coloring and Graph Structure
… set in the plane, and let G be the intersection graph of F . We studied the chromatic number of the complement of G. We also studied the transversal number of F , where the transversal number is the minimum size of a set of points that intersects all convex sets in F .
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Graph designs
… su immersioni, colorazioni e metamorfosi di Graph Designs e una applicazione alle reti.
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Graph Labelings
Given an ordering of the vertices of a graph around a circle, a page is a collection of edges forming non-crossing chords. A book embedding is a circular permutation of the vertices together with a partition of the edges into pages. The pagenumber t (G) is the minimum number of pages in a book …
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Attributed Graph Classification via Deep Graph Convolutional Neural Networks
From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep neural network model which captures the …
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Unified Graph Framework: Optimizing Graph Applications across Novel Architectures
High performance graph applications are crucial in a wide set of domains, but their performance depends heavily on input graph structure, algorithm, and target hardware. Programmers must develop a series of optimizations either on the compiler level, implementing different load balancing or edge …
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Deep Graph Representation Learning and its Application on Graph Clustering
Graphs like social networks, molecular graphs, and traffic networks are everywhere in the real world. Deep Graph Representation Learning (DGL) is essential for most graph applications, such as Graph Classification, Link Prediction, and Community Detection. DGL has made significant progress in …
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Molecular graph Self attention and graph convolution for drug discovery
… development. We model molecules as undirected graphs and use graph convolutions and self-attention to predict molecular properties. With a series of ablation studies, we demonstrate the added value of several key components in our network. We analyze two standard datasets: BBBP, which includes …
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Robust graph transduction
Given a weighted graph, graph transduction aims to assign unlabeled examples explicit class labels rather than build a general decision function based on the available labeled examples. Practically, a dataset usually contains many noisy data, such as the “bridge points” located across different …
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Optical Graph Recognition
Graphs are an important model for the representation of structural information between objects. One identifies objects and nodes as well as a binary relation between objects and edges. Graphs have many uses, e. g., in social sciences, life sciences and engineering. There are two primary …
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Optimal graph learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Graph bisection algorithms
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.
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Causal Graph Summarization
… such tools provide large, nearly-complete causal graphs which are difficult to comprehend, let alone verify to use in causal analysis tasks; as these graphs get bigger and denser with the growth of automated causal discovery methods, domain experts will struggle to comprehend, interpret, and …
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Assembly sequencing through graph reasoning : graph grammar rules for assembly planning
… to automating assembly planning utilizing graph grammars. Computational geometric reasoning is used to produce a label rich graph from a CAD model. This graph is then modified by graph grammar rules to produce candidate assembly sequences which are run in conjunction with a tree search …
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Graph products of groups
… have continued the study of semifree, or graph groups, as they call them. They answer some of the questions left open by the work of Baudisch. It is possible to take the graph analogy a level higher and study graph products of groups, which not only generalise graph groups, but also free …
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Graph minors and algorithms
A graph H is a minor of another graph G, denoted by $H\ {\prec\sb{m}}\ G,$ if a graph isomorphic to H can be obtained from G by a series of vertex deletions, edge deletions, and edge contractions. Graph minors have been studied for several decades as a way of characterizing classes of graphs. …
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