Advanced Graph Theory Course
Dive deep into advanced graph theory, covering centrality measures, spectral analysis, community detection, and dynamic processes like spreading and robustness. Harness practical skills to model real-world networks, execute reliable simulations, and derive actionable insights from intricate graph structures, empowering rigorous mathematical network analysis.

from 4 to 360h 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 measures, and spectral properties. Apply community detection techniques and explore network robustness alongside spreading dynamics. Using real datasets and reproducible methods, gain proficiency in efficient algorithms and precise interpretations for data-driven network studies.
Elevify advantages
Develop skills
- Master centrality: compute, compare, and interpret vital measures swiftly.
- Harness spectral methods: leverage eigenvalues for connectivity, mixing, and epidemic insights.
- Excel in community detection: deploy Louvain, SBM, and spectral clustering on datasets.
- Model robustness: simulate failures, attacks, and disease spread across networks.
- Scale analysis: utilize graph libraries for metrics on vast sparse graphs.
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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