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MATH-448: Statistical analysis of network data
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Lectures in this course (26)
Statistical analysis of network data
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Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.
Extensions to Exchangeability: Statistical Analysis of Network Data
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Covers extensions to exchangeability in network data analysis, including graphon frameworks and edge exchangeability.
Block Models: Continued Analysis
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Explores the stochastic blockmodel, spectral clustering, and non-parametric understanding of blockmodels, emphasizing metrics for comparing graph models.
Nonparametric Network Summaries
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Covers nonparametric network summaries, centrality measures, network modularity, and clustering coefficients.
Distances and Motif Counts
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Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.
Network clustering
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Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.
Latent Space and RDPG: Statistical Analysis of Network Data
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Covers latent space models, logistic regression, RDPG, and real-world networks.
Graph metrics: Statistical analysis
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Explores graph metrics and statistical analysis in network clustering, including ERGMs application in sociology and asymptotics.
Statistical Analysis of Network Data: Noisy Sampled Networks
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Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.
Directed Networks & Hypergraphs
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Explores directed networks with asymmetric relationships and hypergraphs that generalize graphs by allowing edges to connect any subset of nodes.
Hypergraphs and Link Prediction: Statistical Analysis of Network Data
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Covers hypergraphs, complete hypergraphs, link prediction, and scoring methods in network data analysis.
Statistical Analysis of Network Data
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Explores epidemics in network data, covering SIR model, basic reproductive ratio, percolation, directed networks, and maximum likelihood estimation.
Biclustering: Networks MA448
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Explores biclustering in data matrices, identifying coherent behavior patterns and discussing computational methods for analysis.
Statistical Analysis of Networks: Link Prediction and Biclustering
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Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.
Statistical Analysis of Network Data: Hypergraphs
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Introduces hypergraphs, generalizing graphs by allowing subsets of nodes to form edges and exploring their applications in various fields.
Biclustering & latent variables: statistical analysis of network data
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Explores biclustering techniques and latent variables in network data analysis.
Anomalies and Non-exchangeable: Statistical Analysis of Network Data
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Explores anomaly detection in network data and exchangeability extensions for statistical analysis.
Link Prediction: Missing Edges and Probabilistic Methods
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Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.
Statistical Analysis of Network Data: Structures and Models
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Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.
Network Sampling: Consistency, Models, and Dynamics
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Explores network sampling consistency, models, and graph dynamics in real-life scenarios.
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