arXiv:2610.34316v1 Announce Type: new Abstract: Detecting communities, i.e., groups of densely connected nodes, usually requires a central entity that knows the whole network. We study Recursive Neighbor Pruning (RNP), a fully decentralized alternative inspired by social learning. Each agent holds a private opinion but only shows its neighbors a coarse public action, namely its opinion amplified and clipped to a fixed range. Agents repeatedly move their opinion toward the actions of their neighbors and, once opinions settle, drop the links with neighbors whose action has the opposite sign. Repeating this procedure on the remaining network splits it into communities, without any agent knowing the network or the number of communities. We use tools from nonlinear control to characterize this behavior. We show that splitting requires the amplification to exceed a threshold set by how strongly the network is bottlenecked. We also show that a group in which every member has enough of its links inside the group can no longer be split once its members agree, and that a single run separates such groups exactly when started from suitable opinions. Finally, we bound the number of runs and messages. On two standard families of synthetic networks, RNP is as accurate as the best centralized method as long as each node has fewer than about forty percent of its links outside its community, and it fails close to the value predicted by our analysis. It is more accurate than two decentralized competitors below this level, but exchanges many more messages.