Explores Eulerian circuits through the Königsberg bridges problem, leading to the development of graph theory and topology.
Covers the definition of flow in a network and flow calculation.
Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.
Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.
Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.
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.
Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.
