Large-scale network graph representing graph algorithms at production scale

Scaling Graph Algorithms: From Prototypes to Production

TL;DR Graph algorithms are memory-bound, not CPU-bound: a 5 billion node, 50 billion edge graph needs 500+ GB just for adjacency, scores, and working memory - it simply does not fit on one machine Four scaling strategies cover most production cases: distributed processing (vertex-cut or edge-cut partitioning), approximate algorithms (sampling, sketching, early stopping), incremental/streaming approaches, and storage-level optimisation (columnar layout, compression, caching) The hidden costs dominate at scale: communication overhead, synchronisation barriers, debugging distributed state, and the operational burden of keeping it all running Full-graph recompute is not feasible at billions of nodes - incremental algorithms and clever approximations are the norm, not the exception Managed services like Neptune Analytics remove much of the partitioning and operations work, at the price of less control Graph algorithms work great on your laptop. PageRank on a 100,000-node graph finishes in seconds. Louvain finds communities instantly. ...

March 9, 2026 · 8 min · James M
Network graph visualisation representing PageRank and centrality algorithms

Graph Algorithms Explained: PageRank, Centrality, and Why They Matter

TL;DR Graph queries answer relationship questions (paths, positions, influence) that are impossibly expensive in a relational database - the mental shift is from filtering rows to traversing paths PageRank measures a node’s importance by the importance of the nodes pointing to it; it is recursive, converges after 20-30 iterations, and applies far beyond web pages - citations, social influence, recommendations Centrality is an umbrella term: degree (how connected), betweenness (how many paths run through you), closeness (how near everything you are), and eigenvector (whether you are connected to important nodes) each answer a different question Picking the right centrality measure for the question matters more than tuning any single algorithm Managed engines like Neptune Analytics run these at scale without you implementing the iteration loops yourself Graph algorithms often get treated as academic curiosities - something you learn in a computer science course and then never think about again. But they’re actually the hidden backbone of some of the most profitable systems on the internet. ...

January 2, 2026 · 8 min · James M