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Lecture
Network Analysis: Methods and Applications
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Related lectures (32)
Clustering: Unsupervised Learning
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Covers clustering algorithms, evaluation methods, and practical applications in machine learning.
Network clustering
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Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.
Biclustering: Networks MA448
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Explores biclustering in data matrices, identifying coherent behavior patterns and discussing computational methods for analysis.
Stochastic Blockmodel Estimation
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Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.
Social Network Analysis: Modularity Measure
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Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.
Graph Mining: Social Networks Analysis
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Explores graph mining in social networks, covering modularity algorithms and community detection.
Introduction to Image Classification
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Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Statistical Analysis of Network Data: Structures and Models
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Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.
Algorithmic Paradigms for Dynamic Graph Problems
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Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.
Graph Coloring: Theory and Applications
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Explores graph coloring theory, spectral clustering, community detection, and network structures.
Clustering Methods
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Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Clustering & Density Estimation
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Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
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