Bipartite Stochastic Block Models with Tiny Clusters
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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 
where n is the number of vertices in the graph and 
Previous algorithms were only able to identify clusters of size 
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.
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