Lecture

Scale-Free Networks: Power Laws and Preferential Attachment

Related lectures (31)

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

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

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Statistical Analysis of Network Data: Noisy Sampled Networks

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.

Handling Networks: Graph Theory

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Handling Network Data

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

Statistical Analysis of Network Data

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

Social and Information Networks 2: Evolution

Delves into social and information networks dynamics, including herding behavior, information cascades, preferential attachment, and the Friendship Paradox.

Statistical Analysis of Network Data

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

Statistical analysis of network data

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

Heavy-Tailed Distributions

Explores heavy-tailed distributions, the Hill estimator, convergence to Gaussian, and distribution comparison.

Graphical Models: Representing Probabilistic Distributions

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

Directed Networks & Hypergraphs

Explores directed networks with asymmetric relationships and hypergraphs that generalize graphs by allowing edges to connect any subset of nodes.

Network Formation: Random Connection Models

Explores network formation through random connection models and node degree distribution.

Node Degree and Strength

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

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Distributions and Derivatives

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Probability and Statistics

Covers probability distributions, moments, and continuous random variables.

Fixed Points in Graph Theory

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

Networks: Structure and Properties

Explores the structure and properties of networks, including dating and protein networks, small-world effect, hubs, and scale-free property.