Advanced Graph Theory Course
Dive into advanced graph theory, mastering centrality metrics, spectral analysis, community detection, and dynamic processes like spreading. Gain skills to model real-world networks, perform robust simulations, and derive actionable insights from intricate structures using efficient algorithms and datasets.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This course equips you with essential tools for confident analysis of complex networks. Master computing structural metrics, centrality, and spectral properties; apply community detection techniques; and explore network robustness and spreading dynamics. Utilise real-world data and reproducible methods to implement efficient algorithms and deliver precise, data-informed interpretations.
Elevify advantages
Develop skills
- Master centrality measures: compute, compare, and interpret them efficiently.
- Harness spectral tools: apply eigenvalues for connectivity, mixing, and epidemic analysis.
- Excel in community detection: implement Louvain, SBM, and spectral clustering on datasets.
- Model network robustness: simulate failures, attacks, and disease spread effectively.
- 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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