Lecture

Statistical Analysis of Network Data

Related lectures (29)

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

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

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

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

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

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

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

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Explores pseudorandomness and the Expander Mixing Lemma in the context of d-regular graphs.

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

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Explores the Szemerédi Regularity Lemma, e-regularity in bipartite graphs, supergraph structure, and induction techniques.

Covers graphical models for probability distributions and factor graphs representation.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

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