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 75 for “"graph structure"”.
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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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Trustworthiness and the importance of graph structure
… charting the space of all trustworthiness graphs. We address the commonly underestimated importance of the structure of a trust- worthiness graph, and define a space in which to work as well as defining the solvability of a trustworthiness graph. Finally, we provide recommendations for …
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On Induced Subgraphs, Degree Sequences, and Graph Structure
Finally, we define the A4-structure H of a graph G to be the 4-uniform hypergraph on the vertex set of G where four vertices comprise an edge in H if and only if they form the vertex set of an alternating 4-cycle in G. Our definition is a variation of the notion of the P4-structure, a hypergraph …
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A Graph-Structure Transformation Model for Picture-Parsing
Made available in DSpace on 2014-12-10T20:13:35Z (GMT). No. of bitstreams: 1 7310044.pdf: 4291505 bytes, checksum: 4667f90e70fac1e784ed38446a85abfd (MD5) Previous issue date: 1972
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Physics based supervised and unsupervised learning of graph structure
Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to …
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Extremal Problems on Graph Structure, Coding Applications, and Convex Sets
Finally, we also prove an analogue to the Erdo&huml;s-Ko-Rado Theorem on Hamming code.
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LDA based approach for predicting friendship links in live journal social network
… as "friend", "follow", "fan", forming a huge graph structure. The amount of data associated with the users in these Social Networking sites has resulted in opportunities for interesting data mining problems including friendship link and interest predictions, tag recommendations among others. …
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Dynamic Spatio-Temporal Graph Convolutional Networks
… have seen impressive gains in the performance of graph learning as a paradigm for spatial learning problems. Some recent work has explored the intersection of these two fields but often assumes that the underlying graph structure is static. We introduce Dynamic Spatio-Temporal Graph Convolution …
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BEYOND LOCAL NEIGHBORHOODS: LEVERAGING INFORMATIVE NODES FOR IMPROVED GRAPH NEURAL NETWORKS PERFORMANCE
… and scientific domains, can be represented as graphs, where entities are depicted as nodes and their relationships as edges. To analyze the properties of individual entities (node classification) or the community as a whole (graph classification), graph neural networks (GNNs) serve as a …
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Modeling and estimation in Gaussian graphical models : maximum-entropy methods and walk-sum analysis
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fields such as computer vision, image processing, and distributed sensor networks. In this thesis we study two central …
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Approximating the Log-Partition Function
Graphical Models are used to represent structural information on a high-dimensional joint probability distribution. Their expressiveness offers simple reductions from a large number of NP-hard problems to inference tasks such as computing the partition function (exact inference) or approximating …
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Data structures, minimization and complexity of boolean functions
… thesis also examines the complexity issues and graph structure of OBDDs of some special Boolean functions. Moreover, some new data structures for Boolean functions are reported in this thesis. Some functions are found to have constant complexity in the new data structure while having exponential …
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Simulation modelling of spatial problems
… strategy for spatial problems which uses a data structure based on spatial relationships. Using this network based approach, two domain specific data-driven models are developed in which the movement of people is modelled as a quasi-continuous process. The development of simulation modelling …
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Learning common sense knowledge from user interaction and principal component analysis
… Using principal component analysis on the graph structure of ConceptNet yields AnalogySpace, a vector space representation of common sense knowledge. This representation reveals large-scale patterns in the data, while smoothing over noise, and predicts new knowledge that the database should …
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GPU-accelerated Inference for Discrete Probabilistic Programs
… We make two key contributions : (1) a factor graph IR implemented in JAX that supports variable elimination and Gibbs sampling, and (2) a modeling DSL with a compiler that lowers programs to the factor graph IR. Our system enables significant performance optimizations through static analysis …
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Graph Neural Networks for City Policy Recommendations as a Link Prediction Task
Graph Neural Networks (GNNs) have become a widely utilized tool in recommender systems in various contexts. While recommendation tasks can be approached using a multitude of data structures and types, graph-structured data is particularly well-suited for this domain, as graphs naturally capture a …
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Information extraction from digital social trace data with applications to social media and scholarly communication data
Information extraction (IE) aims at extracting structured data from unstructured or semi-structured data. The thesis starts by identifying social media data and scholarly communication data as a special case of digital social trace data (DSTD). This identification allows us to utilize the graph …
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In -Network Computation in Wireless Sensor Networks
… ways of exploiting the network connectivity graph structure in order to speed up computation.
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Exploiting chordal structure in systems of polynomial equations
Chordal structure and bounded treewidth allow for efficient computation in linear algebra, graphical models, constraint satisfaction and many other areas. Nevertheless, it has not been studied whether chordality might also help solve systems of polynomials. We propose a new technique, which we …
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Bayesian network models of biological signaling pathways
… for automatically reverse-engineering the structure of a signaling pathway, from high-throughput data. We apply Bayesian network structure inference to signaling protein measurements performed in thousands of single cells, using a machine called a flow cytorneter. Our de novo reconstruction …
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