Social and Information Networks: Structure

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Related lectures (32)

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Network Analysis: Methods and Applications

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

Clustering: Dimensionality Reduction

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Networks and Brain Networks

Covers the basics of networks, focusing on brain networks, historical breakthroughs, small-world and scale-free network discoveries, and the importance of the human connectome.

Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Network clustering

Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.

Graph Coloring: Theory and Applications

Explores graph coloring theory, spectral clustering, community detection, and network structures.

Clustering: Unsupervised Learning

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Clusters and Communities

Explores clustering, community detection, K-means, GMM, modularity, and the Louvain method.

Stochastic Blockmodel Estimation

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

Biclustering: Networks MA448

Explores biclustering in data matrices, identifying coherent behavior patterns and discussing computational methods for analysis.