Graph data structures

Graph data structures represent complex relationships using nodes and edges to solve high-dimensional routing, clustering, and recommendation problems.

Graphs excel where relational tables fail by treating connections as first-class citizens. Using a collection of vertices (nodes) and edges (links), these structures power Google PageRank, LinkedIn social mapping, and real-time fraud detection. Whether implemented via adjacency lists for memory efficiency or adjacency matrices for fast edge lookups, graphs enable critical path analysis and Dijkstra's shortest-path calculations. They are the backbone of modern knowledge graphs and recommendation engines (like Netflix or Amazon) because they navigate many-to-many relationships with constant-time traversals.

https://neo4j.com/developer/graph-data-science/graph-algorithms/