Laplacian matrix

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Related lectures (31)

Page 3 of 4

Expander Graphs: Properties and Eigenvalues

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

Process Unit Models: Degrees of Freedom

Explores process unit modeling, balance equations, degrees of freedom, and flowsheet solving.

Max-Flow Min-Cut

Explores the Ford Fulkerson algorithm, Max-Flow Min-Cut theorem, Incidence matrix, and network optimization complexity.

Stochastic Blockmodel Estimation

Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

Interlacing Families and Ramanujan Graphs

Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.

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Graphs and Probabilities

Explores the connection between graphs and probabilities, emphasizing modular and super modular probabilities and correlation properties.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Spectral Clustering: Theory and Applications

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Convergence of Adjacency Matrix: Spectral Properties and Consensus Theorem

Explores the convergence of adjacency matrix powers and consensus theorem for primitive and stochastic matrices, emphasizing spectral properties and networked control systems.