Handling Network Data

Related lectures (28)

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

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

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

Explores automorphisms of graphs, focusing on automorphism groups, Cayley-Abels graphs, and quasi-isometry.

Explores automorphism groups in trees and graphs, focusing on ends and types of automorphisms.

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

Explores the structure and properties of networks, including dating and protein networks, small-world effect, hubs, and scale-free property.

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.

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