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 21 for “"Stochastic block model"”.
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The autoregressive stochastic block model with changes in structure
… advent of online social networks. An important model within network analysis is the stochastic block model, which aims to partition the set of nodes of a network into groups which behave in a similar way. This thesis proposes Bayesian inference methods for problems related to the stochastic …
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Spectral analysis in bipartite biregular graphs and community detection
… recover hidden communities in a random network model we call regular stochastic block model. We rely on a technique introduced recently by Massoullie, which we develop here for random regular graphs.
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Consistent community detection in uni-layer and multi-layer networks
… Networks are flexible frameworks that can model many complex systems. In the majority of the network examples dealt with in the literature, the relations between nodes are assumed to be of the same type such as web page linkage, friendship, co-authorship or protein-protein interaction. …
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Community detection in preferential attachment graphs
… of community detection in a new random graph model, which is a generalization of preferential attachment graphs. This model has some features that are more realistic than those of the often-studied stochastic block model (SBM). A message passing algorithm for community detection is derived, …
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Cluster detection in general Markov chains with applications to directed networks
… benchmarks, such as an implementation of the stochastic block model, Lancichinetti-Fortunato benchmarks, and real-world networks. We score the algorithm’s performance against other detection algorithms using validation metrics such as the Rand index. Our findings indicate that our algorithm’s …
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Statistical Methods for Inferring Latent Factors in Biological Networks
… 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 critical role in driving disease …
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Understanding the importance of side information in graph matching problem
… for matching graphs generated using the Stochastic Block Model. We evaluate the proposed algorithms on synthetic as well as real world datasets using various experiments. The experimental results demonstrate the importance of communities as side information especially when the number of …
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Reducibility and computational lower bounds for problems with planted sparse structure
… rank-1 submatrix, sparse PCA and the subgraph stochastic block model. Our results demonstrate that, despite the delicate nature of average-case reductions, using natural problems as intermediates can often be beneficial, as is the case in worst-case complexity. Our main technical contribution …
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Local-access generators for basic random graph models
… in a manner consistent with the random graph model and all previous choices. Local access generators can be useful when studying the local behavior of specific random graph models. Our goal is to design local access generators whose required resource overhead for answering each query is …
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Model Selection for Stochastic Block Models
… for complex systems, networks (graphs) model entities and their interactions as nodes and edges. In many real-world networks, nodes divide naturally into functional communities, where nodes in the same group connect to the rest of the network in similar ways. Discovering such communities …
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Channel Comparison Methods and Statistical Problems on Graphs
… Part I to solve problems related to random graph models with community structures. The random graph models include the stochastic block model (SBM) and its variants, which hold significance in statistics, machine learning, and network science. Central problems for these models ask about the …
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Statistical limits of graphical channel models and a semidefinite programming approach
… of exact recovery in the class of statistical models which can be expressed in terms of graphical channels. In a graphical channel model, we observe noisy measurements of the relations between k nodes while the true labeling is unknown to us, and the goal is to recover the labels correctly. …
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Topics in applied econometrics
… adjust to changes in supply and demand by: (1) modeling the auction market mechanism as a Walrasian mechanism, (2) coarsening the resulting Walrasian market via a stochastic block model, (3) computing the Walrasian equilibrium of this coarsened market through sampling, and (4) using the …
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Community Detection in Complex Networks
The stochastic block model is a powerful tool for inferring community structure from network topology. However, the simple block model considers community structure as the only underlying attribute for forming the relational interactions among the nodes, this makes it prefer a Poisson degree …
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Approximate likelihood for dependent networks and hyperlink predictions
… intercorrelated. However, existing probabilistic models for community detection such as the stochastic block model (SBM) are not designed to capture the dependence among edges. In the first part, we propose a novel community detection approach to incorporate intra-community dependence of …
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Subset sum and community problems: From social to geometry
… in graphs. Towards this end we study the Stochastic Block Model (SBM) on $k$-clusters: a random model on $n=km$ vertices, partitioned in $k$ equal sized clusters, with edges sampled independently across clusters with probability $q$ and within clusters with probability $p$, $p>q$. The goal …
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Improving the output of algorithms for large-scale approximate graph matching
In approximate graph matching, the goal is to find the best correspondence between the labels of two correlated graphs. Recently, the problem has been applied to social network de-anonymization, and several efficient algorithms have been proposed for approximate graph matching in that domain. These …
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Statistical inference on network data
Networks arise from modeling complex systems in various fields, such as computer science, social science, biology, psychology and finance. Understanding and analyzing networks help us better understand these complex systems and extract useful information. In this dissertation, we study problems on …
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Statistical inference for complex networks
… of statistical methods have been proposed for modeling such relational data, identifying community structures, hypothesis testing, and model selection. The majority of these methods dealt with the case where only one network observation is available. However, as the data collection ability …
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Computational and Statistical Detection of High-Dimensional Latent Space Structure in Random Networks
… extent the results and techniques used for these models generalize to other probabilistic latent space graphs. We introduce a new family of probabilistic latent space graphs which we call random algebraic graphs. In random algebraic graphs, Omega is an algebraic group and sigma is compatible with …
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