Computer Scientist Course
Dive into advanced graph algorithms for distance queries and k-closest searches on massive networks. Explore exact BFS baselines and approximate methods like embeddings, sketches, and PageRank. Understand trade-offs in complexity, scalability, and performance tuning. Design and conduct rigorous experiments to evaluate solutions for real-world systems, balancing accuracy and efficiency.

flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
This course provides a thorough, practical approach to mastering graph distance queries and k-closest searches on large networks. Learners will cover formal problem definitions, exact BFS methods, approximate techniques using embeddings, sketches, and PageRank, along with complexity analysis, scalability considerations, and experimental design. Complete the course equipped to select, implement, and assess efficient solutions confidently.
Elevify advantages
Develop skills
- Design graph distance metrics: model, compare, and choose suitable measures for k-closest queries.
- Develop exact search algorithms: create scalable BFS-based methods for fast k-closest results.
- Build approximate indexes: implement sketches, embeddings, and APPR techniques for quick searches.
- Conduct large-scale experiments: plan, execute, and analyse performance tests on big graphs.
- Optimise graph systems: adjust memory usage, parallelism, and data structures for massive datasets.
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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