Centrality

Related lectures (32)

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Explores network analysis methods, operationalizing concepts, historical applications, and challenges in treating time within networks.

Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.

Covers nonparametric network summaries, centrality measures, network modularity, and clustering coefficients.

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

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Explores scale-free networks, power laws, preferential attachment, and network assortativity.

Explores network formation through random connection models and node degree distribution.

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

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

Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.