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 15 of 15 for “"Weighted Networks"”.
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Evolving Weighted Networks to Simulate Epidemics and Lockdowns
… evolutionary algorithm is used to evolve weighted personal contact networks that represent physical contact between individuals, and thus possible paths of infection during an epidemic. The evolutionary algorithm evolves a list of edge-editing operations applied to an initial graph. Two …
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The Friendship Paradox for Weighted and Directed Networks
This thesis studies the friendship paradox for weighted and directed networks, from a probabilistic perspective. We consolidate and extend recent results of Cao and Ross and Kramer, Cutler and Radcliffe, to weighted networks. Friendship paradox results for directed networks are given; connections …
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Bayesian multiple-network multi-layer exponential random graph models (MNML-ERGMs): developing efficient inference and application to neuroimaging
Statistical networks are mathematical representations of vertices connected by edges, which can be binary (present or absent) or weighted (associated with a weight). Studies often analyse topological structures of statistical networks using probabilistic modelling. However, most network models …
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A New Look at Clustering Coefficients with Generalization to Weighted and Multi-Faction Networks
… for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks, the …
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Peer evaluation with graph neural networks
… first model peer assessment as multi-relational weighted networks that can represent a variety of peer assessment setups, and capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph convolutional network which can learn assessment …
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Topology of Social and Managerial Networks
… in recent years, organizational and man- agerial networks have reached high levels of intricacy. These are one of the many complex systems consisting of a large number of highly interconnected heterogeneous agents. The dominant paradigm in the representation of intricate relations between agents …
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Density-based clustering of information networks by substructure optimization
Information networks, such as biological or social networks, contain groups of related entities, which can be identified by clustering. Density-based clustering (DBC) differs from vertex-partitioning methods in that some vertices are classified as noise. This approach is useful in practice to …
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Learning NP-hard problems on networks using Geometric Deep Learning
… from a wide range of domains can be modeled as networks, i.e., a set of nodes that represent the entities of the system and a set of links between nodes that represent relations among them. For instance, social networks can be used to describe how people interact with each other, computer …
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Statistical models and inference for dynamic networks
… often best understood within the framework of networks. Network data can vary in many ways. For example, one might have binary or weighted networks, directed or undirected networks, and static or longitudinal networks. This last type of network, also called a dynamic network, is the focus of …
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Metric Representations Of Networks
The goal of this thesis is to analyze networks by first projecting them onto structured metric-like spaces -- governed by a generalized triangle inequality -- and then leveraging this structure to facilitate the analysis. Networks encode relationships between pairs of nodes, however, the …
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Statistical Methods for Inferring Latent Factors in Biological Networks
… approach for investigating biological systems as networks, enabling researchers to model interactions between components in a comprehensive and systematic manner. A fundamental question in network biology is the estimation of latent factors, such as protein or pathway activity, which play a …
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Changing edges in graphical model algorithms
… in decoding circuits, agents in small-world networks, and neurons in our brains. These structures are often not static and can change over time, resulting in removal of edges, extra nodes, or changes in weights of the links in the graphs. For example, wires in message-passing decoding …
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Characterising disease-related and developmental changes in correlation-derived structural and functional brain networks
… applying the mathematical framework of complex networks to data from magnetic resonance imaging. Connections (edges) in such brain networks are commonly constructed using correlations of features between pairs of brain regions, such as regional morphology (across participants) or …
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Weighted Graph Compression using Genetic Algorithms
Networks are a great way to present information. It is easy to see how different objects interact with one another, and the nature of their interaction. However, living in the technological era has led to a massive surge in data. Consequently, it is very common for networks/graphs to be large. When …
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Community Structure Detection in Complex Biological Networks
… data can be efficiently modelled and analysed. Networks over a natural modelling framework for complex biological systems and as such, network theory and related computational approaches have proven important in bioinformatics. A particular facet of network theory that has been employed to …