Lectures related to Handling Network Data

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

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

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

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

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

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

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

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

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

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

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.