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 reliable simulations, and derive actionable insights from intricate graph structures using efficient algorithms and practical datasets.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Explore advanced graph theory to confidently analyze complex networks. Master computing centrality, spectral properties, and community detection, while modeling robustness and spreading dynamics. Employ efficient algorithms, real datasets, and reproducible methods for precise, data-informed network insights.
Elevify advantages
Develop skills
- Master centrality measures: compute, compare, and interpret them quickly.
- Apply spectral tools: use eigenvalues for connectivity, mixing, and epidemic analysis.
- Perform community detection: use Louvain, SBM, and spectral methods on real data.
- Model network robustness: simulate failures, attacks, and disease spread.
- Conduct scalable analysis: leverage graph libraries for 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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