Lectures related to Scale-free network

Scale-Free Networks: Power Laws and Preferential Attachment

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

Network Sampling: Consistency, Models, and Dynamics

Explores network sampling consistency, models, and graph dynamics in real-life scenarios.

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

Node Degree and Strength

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

Paths, Diffusion, and Navigation

Explores paths in networks, brain connectivity, shortest path routing, network efficiency, navigation, and upcoming midterm.

Virus Propagation and Growing Networks

Explores virus propagation, network resilience, and growing networks dynamics.

The Quest for Universality in Computing

Explores the history of computing, the impact of the consensus problem, and ongoing research to regain universality in large-scale networks.

Latent Space and RDPG: Statistical Analysis of Network Data

Covers latent space models, logistic regression, RDPG, and 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.

Network Formation: Random Connection Models

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

Networks and Brain Networks

Covers the basics of networks, focusing on brain networks, historical breakthroughs, small-world and scale-free network discoveries, and the importance of the human connectome.

Networks: Structure and Properties

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

Introduction to Networks and Brain Networks

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

Graph Coloring: Theory and Applications

Explores graph coloring theory, spectral clustering, community detection, and network structures.

Statistical analysis of network data

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

Social and Information Networks: Structure

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

Graph Machine Learning

Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.

Introduction to Networked Control Systems

Covers classic feedback control, modeling implications, challenges, and opportunities in Networked Control Systems.

General Physics: Quantum - Part 1

Covers combined interference, diffraction, network properties, and resolving power in networks.

General Physics: Quantum - Resolving Power and Diffraction Gratings

Covers the resolving power of networks and properties of diffraction gratings.