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Statistical analysis of network data
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Related lectures (55)
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.
Statistical Analysis of Network Data: Noisy Sampled Networks
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Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.
Graph Theory Fundamentals
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Explores fundamental graph theory concepts, Erdős' results, Chromatic Lemma, and Union Bound theorem in graph theory.
Interlacing Families and Ramanujan Graphs
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Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.
Graphical Models: Probability Distributions and Factor Graphs
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Covers graphical models for probability distributions and factor graphs representation.
Graph Theory and Network Flows
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Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.
Expander Graphs: Properties and Eigenvalues
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Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.
Embedding Graphs into Trees
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Covers embedding graphs into trees with a focus on minimizing distortion and Bartal Tree embeddings.
Cheeger's Inequalities
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Explores Cheeger's inequalities for random walks on graphs and their implications.
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.
Reformulating Problems: Tools and Intuition
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Focuses on open problems and the importance of reformulating problems with better tools and intuition.
Message passing in graphical models
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Explains message passing in graphical models and the matching problem in graph theory.
Cartesian Product: Graph Operations
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Explores the Cartesian product operation on graphs and introduces the concept of cubes.
Belief Propagation on Graphs
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Explores belief propagation on graphs, emphasizing normalization, recursive relations, and iterative computation of the partition function.
Dijkstra's Algorithm Overview
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Demonstrates the iterative process of applying Dijkstra's algorithm to find optimal paths.
Graph Algorithms: Modeling and Representation
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Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.
Shortest Paths: Negative Weights
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Explores Bellman-Ford algorithm for negative weight graphs and currency exchange rates.
Handshake Problem: Graph Representation
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Explores a classic handshake problem at a party using graph representation.
Graph Sketching: Connected Components
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Covers the concept of graph sketching with a focus on connected components.
Graph Theory Basics
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Introduces graph theory basics, Ramsey theory, and graph coloring concepts.
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