Concept

Giant component

Related lectures (14)

Phase Transitions: Connectivity in Random Graphs

Delves into phase transitions in random graphs, emphasizing the emergence of a giant component with varying connectivity.

Phase Transitions: Percolation in 2D Networks

Explores phase transitions through percolation in 2D networks.

Statistical Analysis of Network Data

Explores epidemics in network data, covering SIR model, basic reproductive ratio, percolation, directed networks, and maximum likelihood estimation.

Social and Information Networks: Structure

Explores the structure of social and information networks, focusing on giant components, clustering, tie formation, and network connectivity.

Graph Algorithms: DFS, Topological Sort, SCC

Explores DFS, Topological Sort, SCC in graphs, and introduces Flow Networks with practical examples.

Scale-Free Networks: Power Laws and Preferential Attachment

Explores scale-free networks, power laws, preferential attachment, and network assortativity.

Centrality and Hubs

Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.

Node Degree and Strength

Explores brain node connectivity, node degree, strength, random networks, power law distributions, and the complexity of real networks.

Graph Models and Brain Connectomics

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

Node Degree and Strength

Explores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.

Conditional Statements & Graph Theory

Covers conditional statements and graph theory, including proof techniques and concepts related to graphs, paths, connectivity, and gossip protocols.

Detecting Communities in Random Graphs

Delves into detecting communities in random graphs, exploring fundamental laws, phase transitions, and spectral algorithms.

Neural Signals and Connectomes

Explores neural signals, connectomes, graph theory, and multi-voxel pattern analysis in fMRI trials.

Percolation: Random Graph Models

Covers percolation theory and random graph models, exploring connectivity thresholds and critical power for wireless networks.