Eigenvector centrality

Related lectures (19)

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Explores the construction of Ramanujan graphs using polynomials and addresses challenges with the probabilistic method.

Explores centrality, hubs, eigenvectors, clustering coefficients, small-world networks, network failures, and percolation theory in brain networks.

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Explores the application of matrices and eigendecompositions in networks.

Explains PageRank and HITS algorithms for web page ranking and authority calculation.

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

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

Covers Cheeger's inequalities and the combinatorial properties of graphs.

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.