Graph Algorithms with Rust
About this course
Graph Algorithms with Rust teaches you to model real datasets as graphs and run the classical algorithms — BFS, DFS, Dijkstra, PageRank, and Kosaraju strongly-connected components — in cache-friendly Rust. Across five modules you walk through the same problems data engineers actually solve: loading edge lists into a graph, finding the shortest walking route between Lisbon landmarks, ranking sports websites by PageRank, scoring UFC fighters by centrality, and detecting communities in a Twitter-style follower graph. You use both the textbook petgraph crate and the benchmarked aprender-graph crate, so you see two production-tested ways to model the same problem. Every algorithm comes with a runtime contract — provable assertions like "PageRank scores must sum to 1.0" — so the demos catch silent regressions, not just compile errors. The course closes with a working clap-based CLI tool that wires every algorithm together behind subcommands and emits machine-readable JSON, ready to ship as a single static binary. By the end you can pick the right algorithm for a real graph problem and ship it as a tested Rust binary.
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What you'll learn
- model real datasets as graphs
- implement graph algorithms such as BFS and Dijkstra
- create a command-line tool in Rust
- utilize the petgraph and aprender-graph crates
- understand runtime contracts for algorithms
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