Networks: Computer Representation

Related lectures (29)

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Introduces graph theory basics, graph representation methods, and traversal algorithms like BFS and DFS.

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

Covers graphical models for probability distributions and factor graphs representation.

Explores the connection between graphs and probabilities, emphasizing modular and super modular probabilities and correlation properties.

Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.

Explores optimal binary search trees to minimize expected search cost and discusses graphs representation using adjacency matrices and lists.

Covers indexing techniques, distributed retrieval algorithms, and challenges in large-scale web indexing.

Covers the spatial response of systems, including the calculation of the center of mass and convolution.

Explores consensus with GR nodes in networked control systems, focusing on condensation digraphs and the main result.

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