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 58 for “"graph models"”.
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Graph Models and Shape Deformation for Image Segmentation
The second part of this thesis is focused on using prior geometric information to improve the reliability and accuracy of image segmentation. Specifically, a new shape-deformation method is proposed to incorporate prior template shape information into image segmentation by deforming a given …
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Local-access generators for basic random graph models
Consider a computation on a massive random graph: Does one need to generate the whole random graph up front, prior to performing the computation? Or, is it possible to provide an oracle to answer queries to the random graph "on-the-fly" in a much more efficient manner overall? That is, to provide a …
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Inferring differences between networks using Bayesian exponential random graph models
… in the study. Most statistical network models, however, were originally proposed to describe a single underlying relational structure such as friendships between individuals or hyperlinks between web pages. As a result, the development of these models has largely been restricted to the …
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Physical meaning of zeros and transmission zeros from bond graph models
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1995.
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The use of exponential random graph models to explore social foraging dynamics of interspecific songbird assemblages
… testing. One such advance is exponential random graph models (ERGMs). Developed for the social sciences, ERGMs analyze how network structures (i.e., configurations of edges) and attributes of nodes and edges affect the formation of edges. This allows practitioners to explore how different …
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Bayesian multiple-network multi-layer exponential random graph models (MNML-ERGMs): developing efficient inference and application to neuroimaging
… probabilistic modelling. However, most network models focus on describing a single binary network. The exponential random graph model (ERGM) belongs to such models, which characterise the distribution of networks through a set of network summary statistics. Each summary statistic provides a …
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Exponential Random Graphs and a Generalization of Parking Functions
<p>Random graphs are a powerful tool in the analysis of modern networks. Exponential random graph models provide a framework that allows one to encode desirable subgraph features directly into the probability measure. Using the theory of graph limits pioneered by Borgs et. al. as a foundation, we …
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Infrastructure for modeling and inference engineering with 3D generative scene graphs
… the development of probabilistic generative models for visual perception using a rich abstract representation of 3D scene geometry called a scene graph. However, there remain several challenges in the practical implementation of scene graph models, including human-editable specification, …
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High-dimensional and dependent data with additional structure
… This thesis considers two widely used classes of models for high-dimensional and dependent data with additional structure, high-dimensional multivariate time series and exponential-family random graph models. In the case of high-dimensional multivariate time series, there is often additional …
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Topological Deep Learning: Graphs, Complexes, Sheaves
The types of spaces where data resides - graphs, meshes, grids, manifolds - are becoming increasingly varied and heterogeneous. Therefore, translating ideas, models, and theoretical results between different domains is becoming more and more challenging. Nonetheless, two fundamental principles …
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Resource Management for Distributed Memory Multicomputers
… of algorithms were developed for different task graph models. We first present a suite of static resource management algorithms. For acyclic graphs, the LAST algorithm provides fast processor allocation and intelligent processor usage. For nondeterministic task graphs, the remap algorithm uses …
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Channel Comparison Methods and Statistical Problems on Graphs
… these tools to statistical problems related to graphs. Part I focuses on information channels and channel comparison methods, including f-divergences, strong data processing inequalities, and preorders between channels. While these theories have been well-established for binary memoryless …
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Direct Computations of Spatially Resolved Viscoelastic Moduli of Biomolecular Condensates
… In this study, we develop a modified graph Laplacian-based collective model to characterize viscoelastic heterogeneity within condensates based on results of lattice-based Metropolis Monte Carlo (MMC) simulations. By integrating random graph models and simulations of A1-LCD, a type of …
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Structure and evolution of communication networks in organizations
… Procedure (MRQAP) and Exponential Random Graph Models (ERGM), we identify significant factors affecting the size and shape of communication networks. These parameters include organizational structure, homophily, job position, and physical proximity. We record the evolution of the networks …
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Analyzing The Community Structure Of Web-like Networks: Models And Algorithms
… sparse structure with certain small, dense subgraphs occurring much more frequently than they do in the classical Erdös-Rényi random graphs. This peculiarity--which is commonly referred to as community structure--has been observed in seemingly unrelated networks such as the Web, …
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A Network Approach to Nuclear Deterrence
… system instead of traditional game theoretical models. Four networks are created representing ties of influence on states in the international system based on the four dimensions of DIME (Diplomacy, Information, Military, and Economics). Core states in each network are identified through degree …
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Sub-linear algorithms for graph problems
… face of massive data sets, classical algorithmic models, where the algorithm reads the entire input, performs a full computation, then reports the entire output, are rendered infeasible. To handle these data sets, alternative algorithmic models are suggested to solve problems under the restricted, …
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MACHINE LEARNING FOR TEMPORAL HETEROGENEOUS GRAPHS: PREDICTIVE METHODS, INTERPRETABILITY AND APPLICATIONS.
… predictive methods for temporal heterogeneous graphs by integrating modeling and tools from network science and graph deep learning. We introduce discrete-time graph learning architectures based on Graph Neural Networks (GNNs) and linear scoring functions tailored for forecasting dynamic, …
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Efficiently Learning Monotone Decision Trees with ID3
… form expressions. I modeled these functions as graphs and examined each type of connected component contained in these models, i.e. path graphs and cycle graphs. I determined the influence of the variables in the pieces of these graph models in order to understand how ID3 behaves when learning …
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