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In this course, you will learn:
- Introduces complex networks, their structure, and function, using examples from engineering, applied mathematics, and social sciences.
- Topics covered include spectral graph theory, centrality, random graph models, contagion, cascades and dissemination, and opinion dynamics.
Syllabus:
- Course specifics, motivation, and intro to graph theory
- Introduction to graph theory
- Strong and weak ties, triadic closure, and homophily
- Centrality measures
- Centrality and web search, spectral graph theory
- Spectral graph theory, spectral clustering, and community detection I
- Spectral graph theory, spectral clustering, and community detection II
- Network models I
- Network models II
- Network models III
- Configuration model and small-world graphs
- Growing networks
- Linear dynamical systems
- Markov chains / Information spread and distributed computation
- Learning and herding
- Epidemics
- Introduction to game theory I
- Introduction to game theory II / Application of game theory to networks
