Lecture

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

Related lectures (31)

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Statistical Analysis of Network Data: Structures and Models

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

Stochastic Blockmodel Estimation

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

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Handling Networks: Graph Theory

Covers the fundamentals of handling networks and centrality measures in graph theory.

Distances and Motif Counts

Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.

Handling Networks: Graph Theory

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Centrality and Hubs

Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.

Centrality and Hubs

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

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Statistical Analysis of Network Data

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Exchangeability and Network Statistics

Explores exchangeability, statistical summaries for networks, invariance issues, and the Poisson Limit theorem in network statistics.

Statistical Analysis of Network Data: Noisy Sampled Networks

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.

Block Models: Continued Analysis

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

Network Analysis: Methods and Applications

Explores network analysis methods, operationalizing concepts, historical applications, and challenges in treating time within networks.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Cayley Graphs: Properties and Applications

Explores the properties and applications of Cayley graphs in group theory and network analysis.

Epidemic Spreading Models

Covers classical models of epidemic spreading and dynamics on networks with examples.

Statistical Analysis of Networks: Link Prediction and Biclustering

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

Social Network Analysis: Modularity Measure

Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.