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Minimax optimal clustering of bipartite graphs with a generalized power method

Guillaume Braun,Hemant Tyagi

Published 2022 in Information and Inference A Journal of the IMA

ABSTRACT

Clustering bipartite graphs is a fundamental task in network analysis. In the high-dimensional regime where the number of rows $n_{1}$ and the number of columns $n_{2}$ of the associated adjacency matrix are of different order, the existing methods derived from the ones used for symmetric graphs can come with sub-optimal guarantees. Due to increasing number of applications for bipartite graphs in the high-dimensional regime, it is of fundamental importance to design optimal algorithms for this setting. The recent work of Ndaoud et al. (2022, IEEE Trans. Inf. Theory, 68, 1960–1975) improves the existing upper-bound for the misclustering rate in the special case where the columns (resp. rows) can be partitioned into $L = 2$ (resp. $K = 2$) communities. Unfortunately, their algorithm cannot be extended to the more general setting where $K \neq L \geq 2$. We overcome this limitation by introducing a new algorithm based on the power method. We derive conditions for exact recovery in the general setting where $K \neq L \geq 2$, and show that it recovers the result in Ndaoud et al. (2022, IEEE Trans. Inf. Theory, 68, 1960–1975). We also derive a minimax lower bound on the misclustering error when $K=L$ under a symmetric version of our model, which matches the corresponding upper bound up to a factor depending on $K$.

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