Statistical Analysis of Network Data: Structures and Models

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (28)

Page 1 of 3

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

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Fixed Points in Graph Theory

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

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Graph Neural Networks: Interconnected World

Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Graph Algorithms: Modeling and Traversal

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Independence Polynomial of Dependency Graph

Covers the independence polynomial of a dependency graph and related concepts such as graph coloring and directed graph properties.

Handling Networks: Graph Theory

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

Stochastic Blockmodel Estimation

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