Advanced Graph Theory Course
This course builds expertise in advanced graph theory, covering centrality metrics, spectral analysis, community detection, and network spreading dynamics. Participants gain skills to model real-world networks, conduct reliable simulations, and derive practical insights from intricate graph structures using efficient tools and datasets.

flexible workload from 4 to 360h
valid certificate in your country
What will I learn?
Gain hands-on skills in advanced graph theory to confidently tackle complex networks. Master computing structural metrics, centrality, and spectral properties; perform community detection; and explore network robustness and spreading phenomena. Utilise real datasets and reproducible methods to implement efficient algorithms and interpret results for data-driven analysis.
Elevify advantages
Develop skills
- Master centrality measures: compute, compare, and interpret them quickly.
- Use spectral tools: apply eigenvalues for connectivity, mixing, and epidemic analysis.
- Apply community detection: use Louvain, SBM, and spectral clustering on real data.
- Model network robustness: simulate failures, attacks, and disease spread.
- Perform scalable analysis: leverage graph libraries for large sparse networks.
Suggested summary
Before starting, you can change the chapters and 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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