Explores the Matrix Tree Theorem and its application in calculating spanning trees in graphs.
Covers the concept of weighted graphs and the Greedy algorithm for finding a minimal spanning tree.
Covers network flow algorithms, including Max Flow, Min Cut, and Negative Cost Cycle Algorithm, progressing from basic definitions to advanced algorithms like Bellman-Ford and Dijkstra's.
Explores graphs and matrices, including adjacency, degree, and Laplace matrices, Matrix-tree theorem, and spanning trees.
Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.
Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.
