Advanced Graph Theory Course
This course builds expertise in advanced graph theory, covering centrality metrics, spectral analysis, community detection, and dynamic processes like spreading. Participants gain skills to model real-world networks, execute simulations, and derive actionable insights from complex data using efficient algorithms and reproducible methods.

4 to 360h flexible workload
certificate valid in your country
What will I learn?
Explore advanced graph theory to analyze networks confidently. Compute centrality, spectral properties, and structural metrics; apply community detection algorithms; and model robustness against failures and spreading dynamics. Use real datasets and scalable tools for precise, reproducible network insights.
Elevify advantages
Develop skills
- Master centrality measures: compute, compare, and interpret them efficiently.
- Apply spectral tools: leverage eigenvalues for connectivity, mixing, and epidemic analysis.
- Perform community detection: use Louvain, SBM, and spectral methods on real datasets.
- Model network robustness: simulate failures, attacks, and disease spread.
- Conduct scalable analysis: apply graph libraries to large sparse networks.
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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