Network graph showing clusters of connected nodes representing community detection

Community Detection Algorithms: Finding Clusters and Groups in Network Data

TL;DR A community is a group of nodes with more internal connections than you would expect by chance; community detection finds these groups purely from network structure, not features Modularity is the metric everything uses - 0.3-0.7 is typical for real networks, and 0.9+ usually means you have over-clustered Louvain is the industry standard: fast, greedy, no hyperparameters to tune, and it produces high-modularity divisions; label propagation, spectral clustering, and Girvan-Newman fill the gaps The same technique powers friend-group detection, product clustering, fraud-ring identification, and finding functional modules in biological networks Overlapping communities remain the genuinely hard problem - most standard algorithms assign each node to exactly one group If you’ve ever looked at a social network and wondered “why is this group of people more connected to each other than to the rest of the network?”, you’ve just articulated the community detection problem. ...

February 9, 2026 · 7 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