Optimal Cluster Recovery in the Labeled Stochastic Block Model

NeurIPS 2016 Se-Young YunAlexandre Proutiere

We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number $K$ of clusters of sizes linearly growing with the global population of items $n$. Every pair of items is labeled independently at random, and label $\ell$ appears with probability $p(i,j,\ell)$ between two items in clusters indexed by $i$ and $j$, respectively... (read more)

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