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Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.
Expander Graphs: Properties and Eigenvalues
Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.
Graphical Models: Representing Probabilistic Distributions
Covers graphical models for probabilistic distributions using graphs, nodes, and edges.
Network Flows Meets Simplex
Explores network flows, simplex method, linear programming, tree solutions, and dual solutions in optimization problems.
Max-Flow Min-Cut
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Graphical Models: Probability Distributions and Factor Graphs
Covers graphical models for probability distributions and factor graphs representation.
Statistical Analysis of Network Data
Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.
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