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 42 for “"Adjacency matrix"”.
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IMPROVING MULTI-VARIATE TIME SERIES FORECASTING WITH DYNAMIC MULTI-HEAD ATTENTION ADJACENCY MATRIX
… novel framework that uses a dynamically learned adjacency matrix based on related work called the Multi-variate Time-series Graph Neural Network(MTGNN). Instead of using a correlation-based learned adjacency matrix, the adjacency matrix and graph modules are replaced with a dynamically learned …
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Polynomials of the Adjacency Matrix of a Graph (distance-Transitive, Distance-Regular, Orbit)
… = A((DELTA)) where A((GAMMA)) and A((DELTA)) are adjacency matrices. For several interesting classes of graphs it is possible to determine all of the graphs which can be generated by a polynomial. Define the ith distance graph, (GAMMA)(,i), as the graph with the same vertex set as (GAMMA) and two …
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Algebraic methods in graph theory
… In general, we can use the eigenvalues of the adjacency matrix of a graph to study various properties of graphs. In this thesis, we obtain the whole spectrum of a family of graphs called Wenger graphs Wm (q ). We also study the a conjecture of Brouwer, concerning the second connectivity of …
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A general computational tool for structure synthesis
… is represented by a graph and further by the adjacency matrix; and (2) instead of only exploiting the eigenvalue of the adjacency matrix, both the eigenvalue and the eigenvector are exploited; specifically the components of the eigenvector have been found very useful in algorithm development. …
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Intersection representations of graphs and digraphs
… is a unit interval digraph if and only if its adjacency matrix is free of six forbidden submatrices: three 3 by 4 matrices and their transposes.
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Spectrum of some regular graphs with widely spaced modifications
… trees, we show that the eigenvalues of the adjacency matrix and Laplacian matrix have high multiplicities. As the trees grow, the graphs of those eigenvalues approach a piecewise-constant "Cantor function", which is different from the corresponding properties of the infinite tree. The second …
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Matrix completion algorithms with applications in biomedicine, e-commerce and social science
This thesis investigates matrix completion algorithms with applications in biomedicine, e-commerce and social science. In general, matrix completion algorithms work well for low rank matrices. Such matrices find many applications in recommender systems and social network analysis. On the other …
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Implementing Quantum Gates With Length-3 Dynamic Graphs
… equation. With the Hamiltonian equal to the adjacency matrix of a sequence of graphs, called a dynamic graph, continuous-time quantum walks have been shown to implement quantum gates, including the T gate, Hadamard gate, and the Controlled{NOT gate. Since these gates make up a universal set …
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A study of knot-graphs
… and extensions of them. The basic tool is the adjacency matrix. In fact several kinds of adjacency matrix are defined, for knot diagrams which are either nonoriented or are given orientations which reflect certain topological properties of the knots concerned. First the properties of knot-graph …
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Spectral Properties of Quaternionic Unit Gain Cycles
… define certain concepts on these graphs such as adjacency and Laplacian matrices, gains of paths, and more. If we restrict ourselves to the unit norm quaternions, we can define quaternionic unit gain graphs, or U(H)-gain graphs, as gain graphs where the domain of the gain function is the unit …
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Leveraging Process Mining and Deep Learning to Improve Health and Safety Outcome Predictive Models
… safety. The first contribution introduces the Adjacency Matrix Deep Learning Prediction (AXDP) Model, which combines graph theory and deep learning to forecast sequential events. AXDP demonstrated superior performance on eight public datasets, outperforming existing models in prediction …
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Properties and Recent Applications in Spectral Graph Theory
… aspects of graphs, such as the walks and the adjacency matrix are explored. In addition, bipartite graphs are discussed along with properties that apply strictly to bipartite graphs. The main focus is on the characteristic polynomial and the eigenvalues that it produces, because most of the …
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Estimation and Inference for Network Data
… models, using the eigenvalues of the normalized adjacency matrix. We also show how this testing method is applicable to cases where the researcher only has access to ARD. Finally, we consider the problem of obtaining low-dimensional representations of objects from dissimilarity data. We propose a …
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Learning from Geometry
… on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of …
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Security in network games
… gap between the 1st and 2nd eigenvalues of the adjacency matrix. Intuitively high expansion ensures redundant connectivity. We also present results simulating our algorithm on networks of size up to $2^{25}$. In the second part of this dissertation we consider the virus inoculation game which …
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Multimedia Big Data Analytics and Fusion for Data Science
… Laplacian and automatically generates the graph adjacency matrix based on a data-driven method. In addition, a dynamic multi-task learning method is introduced to optimize the model training progress by dynamically adjusting the loss weights assigned to each task. It systematically monitors the …
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Multilevel spectral clustering : graph partitions and image segmentation
… In addition, I present a scheme to construct the adjacency matrix, W and degree matrix, D for the coarse graphs. The proposed multilevel graph partitioning algorithm is able to bisect a graph (k = 2) with significantly shorter time than segmenting the original graph without the multilevel …
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Some Advances in Statistical modeling of Brain Structural Connectomes
… These networks are typically expressed as adjacency matrices, with each cell containing a summary of connectivity between a pair of brain regions. There is an emerging statistical literature describing methods for the analysis of such multi-network data in which nodes are common across …
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Spectral analysis of neutral evolution
… and eigen- value, respectively, of the network's adjacency matrix. However, little progress has been made towards understanding the manner in which the topology of the neutral network influences the resulting population distribution and robustness. In this work, we build on recent results from …
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Infection processes on networks with structural uncertainty
… network topology via the spectral radius of the adjacency matrix of the network. It is rare, however, that one has complete information about every potential disease-transmitting interaction; this uncertainty in the network structure is often ignored in deterministic models. Neglecting this …
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