Advanced Graph Theory Course
Master advanced graph theory through centrality, spectral methods, community detection, and spreading processes. Learn to model real networks, conduct robust simulations, and transform complex structures into clear, actionable mathematical insights. Gain proficiency in analysing networks with practical tools and real-world applications for confident, data-driven decisions.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This Advanced Graph Theory course equips you with practical tools to analyse complex networks confidently. You will calculate structural metrics, centrality measures, and spectral quantities, apply community detection, and examine robustness and spreading processes. Using real-world datasets and reproducible workflows, you will master efficient algorithms and clear interpretations for rigorous, data-driven network analysis.
Elevify advantages
Develop skills
- Master centrality: compute, compare, and interpret key measures swiftly.
- Harness spectral tools: apply eigenvalues to assess connectivity, mixing, and epidemics.
- Detect communities: implement Louvain, SBM, and spectral clustering on real data.
- Model robustness: simulate failures, attacks, and epidemic spread in networks.
- Perform scalable analysis: leverage graph libraries for metrics on large sparse graphs.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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