Graph theory

Related lectures (31)

Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Explores the ubiquity of graphs in modern data and analytics, focusing on the shift in organizations' perception of graph technologies.

Explores finding the equation of the tangent line to a function's graph at a point.

Covers sparsest cut, NP-completeness, Bougains Theorem, and concurrent flow in graphs.

Explores concurrent programming theory and practice, covering system models, cache coherence, and graph processing.

Explores causal inference through learning graph structures for causal reasoning from observational data.

Introduces graph theory basics, graph representation methods, and traversal algorithms like BFS and DFS.

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

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