Lectures related to Epidemic Spreading Models

Handling Network Data

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

Graphical Models: Representing Probabilistic Distributions

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

Stein Algorithm: Polynomial Identity Testing

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Handling Networks: Graph Theory

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

Graph Algorithms: Modeling and Traversal

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

Handling Networks: Graph Theory

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

COVID-19 Outbreak: Epidemiology and Mitigation

Explores the COVID-19 outbreak, its terminology, transmission, severity, and global impact, emphasizing the importance of mitigation strategies and digital epidemiology.

Handling Network Data

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

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.

Graphs: Properties and Representations

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Approximation Algorithms

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Epidemics and Bootstrap Percolation in Square Lattice

Explores epidemics spread models and Bootstrap Percolation in square lattice networks, focusing on the Kolmogorov equation and probability generating functions.

Stochastic Analysis & Epidemic Containment Models

Delves into stochastic analysis, contract theory, and epidemic models, exploring uncertainties and interventions in epidemiology.

Graph Theory and Network Flows Log in to Mediaspace to watch this video

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Statistical analysis of network data Log in to Mediaspace to watch this video

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

Statistical Analysis of Network Data: Structures and Models Log in to Mediaspace to watch this video

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Graphs and Networks: Basics and Applications Log in to Mediaspace to watch this video

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Algorithmic Paradigms for Dynamic Graph Problems Log in to Mediaspace to watch this video

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Graphs in Deep Learning: Applications and Techniques Log in to Mediaspace to watch this video

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.