Lecture

Social and Information Networks: Structure

Related lectures (35)
Statistical analysis of network data
Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.
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.
Epidemic Spreading Models
Covers classical models of epidemic spreading and dynamics on networks with examples.
Graph Coloring: Theory and Applications
Explores graph coloring theory, spectral clustering, community detection, and network structures.
Network Analysis: Methods and Applications
Explores network analysis methods, operationalizing concepts, historical applications, and challenges in treating time within networks.
Stochastic Blockmodel Estimation
Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.
Network Formation: Random Connection Models
Explores network formation through random connection models and node degree distribution.
Graph Models and Brain Connectomics
Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.
Handling Network Data
Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.
Handling Network Data
Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.