Move beyond simple lookups to learn how graph traversal really works. You will implement breadth-first and depth-first search in Rust, run shortest-path queries with Dijkstra and A* against Neptune, and reason about the trade-offs between recursive Cypher and client-side traversal. By the end of this module, you will be able to choose the right traversal strategy for a given problem and implement it in production-quality Rust.

Learn how to identify the most important nodes in a graph. You will compute degree, betweenness, and closeness centrality, then implement PageRank from scratch using power iteration over an eigenvector formulation. By the end of this module, you will be able to rank nodes by influence in real-world networks and explain the linear algebra that makes PageRank work.

Discover the structure hidden inside large, messy graphs. You will implement Tarjan's and Kosaraju's algorithms for strongly connected components, then apply Louvain modularity to find communities in undirected networks. By the end of this module, you will be able to decompose a real-world graph into its meaningful subgroups and explain what those subgroups reveal about the system being modeled.

Take everything you have built and ship it as a real tool. You will design a robust command-line interface in Rust, add structured logging and error handling, integrate with CI, and package the binary for distribution. By the end of this module, you will have a production-ready Rust CLI that runs graph algorithms against Neptune from your terminal and is ready to hand off to a team.