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Networks, Flows
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Related lectures (32)
Graph Theory and Network Flows
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Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.
Network clustering
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Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.
Statistical Analysis of Network Data
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Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.
Sparsest Cut: ARV Theorem
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Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.
Graph Theory: Girth and Independence
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Covers girth, independence, probability, union bound, sets, and hypergraph recoloring.
Cheeger's Inequalities
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Explores Cheeger's inequalities for random walks on graphs and their implications.
Assembly: Physics of Manufacturing
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Discusses the importance of assembly in manufacturing and covers common couplings, stability, and spatial vectors.
Interlacing Families and Ramanujan Graphs
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Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.
Message passing in graphical models
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Explains message passing in graphical models and the matching problem in graph theory.
Max Sum Diversification
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Explores maximizing diversity in document selection, graph clique determination, theorems on negative type, and convex optimization.
Interlacing Families and Ramanujan Graphs
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Explores interlacing families of polynomials and 1-sided Ramanujan graphs, focusing on their properties and construction methods.
Belief Propagation on Graphs
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Explores belief propagation on graphs, emphasizing normalization, recursive relations, and iterative computation of the partition function.
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