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Label Propagation — Community Detection by Majority Vote

Watch the Raghavan–Albert–Kumara label propagation algorithm find communities in a 3D network: every node repeatedly adopts the label held by the majority of its neighbours until the labels stop changing.

Networks & Graph Theory3DModerate60 FPS📱 Mobile-adapted⇄ 2D version

This simulator generates a 3D network with ground-truth clusters (a stochastic block model) and runs the real label propagation algorithm on it live: every node starts with its own unique label, and on each sweep every node switches to whichever label the majority of its neighbours currently hold. There is no modularity score being maximized and no global view of the graph — just local majority votes — yet the labels reliably converge into the same handful of communities the graph was built from, visible as the node palette collapsing from dozens of colours down to a few. Tune the node count, the number of planted communities and how blurred the boundaries between them are, then step through sweeps (or auto-run them) and watch the iteration count, live label count, per-step change count and modularity settle as the algorithm converges.

⚙ Under the hood

Watch the Raghavan–Albert–Kumara label propagation algorithm find communities in a 3D stochastic-block-model network: every node repeatedly adopts the label held by the majority of its neighbours, with no global objective, until the labels stop changing.

network analysiscommunity detectiongraph algorithmlabel propagationdata sciencestochastic block model

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install