Epidemic Spreading Models

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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.

Graphs and Networks: Basics and Applications

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

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.

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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 in Deep Learning: Applications and Techniques

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

Statistical Analysis of Network Data

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

Graphs: Properties and Representations

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