## Bipartite Stochastic Block Models with Tiny Clusters

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

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### Conference Event Type: Poster

### Abstract

We study the problem of finding clusters in random bipartite graphs. We present a simple two-step algorithm which provably finds even tiny clusters of size $O(n^\epsilon)$, where $n$ is the number of vertices in the graph and $\epsilon > 0$. Previous algorithms were only able to identify clusters of size $\Omega(\sqrt{n})$. We evaluate the algorithm on synthetic and on real-world data; the experiments show that the algorithm can find extremely small clusters even in presence of high destructive noise.