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Related lectures (32)
Graph Models and Brain Connectomics
Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.
Topology in Complex Networks: Insights from Topological Data Analysis
Explores the role of higher-order topological properties in complex networks using topological data analysis for structural break and price anomaly detection.
Non-parametric regression for networks
Explores non-parametric regression for networks, covering object data analysis, network graphs, extrinsic distances, and practical projections.
Introduction to Networks and Brain Networks
Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.
Handling Networks: Graph Theory
Covers the fundamentals of handling networks and centrality measures in graph theory.
Dynamic Programming: Shortest Paths Algorithms
Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.
Networked Control Systems: Opportunities
Explores coordination in networked control systems, graph theory, and consensus algorithms.
Belief Propagation
Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
Graph Algorithms II: Traversal and Paths
Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.
Fixed Points in Graph Theory
Focuses on fixed points in graph theory and their implications in algorithms and analysis.
Networks: Trees
MOOC: Optimization: principles and algorithms - Network and discrete optimization
Explains the concept of trees in graph theory and the definition of a spanning tree.
Unweighted Bipartite Matching
Introduces unweighted bipartite matching and its solution using linear programming and the simplex method.
Graph Theory: Path Weighted by Amplitude
Covers the calculation of paths in a graph, focusing on amplitude-weighted paths.
Minimum Guardians in Graphs
Explores the concept of minimizing guardians in graphs through strategic edge selection.
Cavity Method: Mean Field Theory
Explores the Cavity Method in Mean Field Theory, analyzing spins in an external field within a graph.
Polynomial Identity Testing
Covers polynomial identity testing using oracles and random point evaluation, with applications in graph theory and algorithmic aspects.
Probabilistic method and its applications
Introduces the probabilistic method to prove existence results in graph theory.
Stochastic Block Model
Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Statistical analysis of network data
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Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.
Distances and Motif Counts
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Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.
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