Networks, Flows

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Related lectures (32)

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Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Graph Models and Brain Connectomics

Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Distances and Motif Counts

Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Topology in Complex Networks: Insights from Topological Data Analysis

Explores the role of higher-order topological properties in complex networks using topological data analysis for structural break and price anomaly detection.

Network clustering

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

Non-parametric regression for networks

Explores non-parametric regression for networks, covering object data analysis, network graphs, extrinsic distances, and practical projections.

Introduction to Networks and Brain Networks

Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.

Statistical Analysis of Network Data

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Handling Networks: Graph Theory

Covers the fundamentals of handling networks and centrality measures in graph theory.