Nonparametric Network Summaries

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Related lectures (32)

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Graph Statistics: Random Graphs, Graph Homomorphisms, and Network Analysis

Explores graph statistics, random graph generation, network analysis, centrality measures, and clustering coefficients.

Clustering: K-means & LDA

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Stochastic Blockmodel Estimation

Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

Spectral Clustering: Theory and Applications

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Centrality and Hubs

Explores centrality, hubs, eigenvectors, clustering coefficients, small-world networks, network failures, and percolation theory in brain networks.

Statistical Analysis of Network Data: Structures and Models

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Block Models: Continued Analysis

Explores the stochastic blockmodel, spectral clustering, and non-parametric understanding of blockmodels, emphasizing metrics for comparing graph models.

Clustering: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.