Distance (graph theory)

Related lectures (32)

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Explores coordination in networked control systems, graph theory, and consensus algorithms.

Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.

Explores the design of graph weights for consensus and applications in sensor networks.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Covers embedding graphs into trees with a focus on minimizing distortion and Bartal Tree embeddings.

Explores consensus in networked control systems through graph weight design and matrix properties.

Covers the calculation of paths in a graph, focusing on amplitude-weighted paths.

Discusses the importance of assembly in manufacturing and covers common couplings, stability, and spatial vectors.

Covers the definition of flow in a network and flow calculation.

Explores non-parametric regression for networks, covering object data analysis, network graphs, extrinsic distances, and practical projections.