Graphical Models: Representing Probabilistic Distributions

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Covers the concept of graphical models and joint probability distributions.

Explores directed networks with asymmetric relationships and hypergraphs that generalize graphs by allowing edges to connect any subset of nodes.

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

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

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

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

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.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

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