Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE

Jeonghwan Lee; Daesung Kim; Hye Won Chung

doi:10.1109/jsait.2020.3037170

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arXiv · 2003.10038

Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE

Jeonghwan Lee·Daesung Kim·Hye Won Chung

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Abstract

We study hypergraph clustering in the weighted $d$-uniform hypergraph stochastic block model ($d$\textsf{-WHSBM}), where each edge consisting of $d$ nodes from the same community has higher expected weight than the edges consisting of nodes from different communities. We propose a new hypergraph clustering algorithm, called \textsf{CRTMLE}, and provide its performance guarantee under the $d$\textsf{-WHSBM} for general parameter regimes. We show that the proposed method achieves the order-wise optimal or the best existing results for approximately balanced community sizes. Moreover, our results settle the first recovery guarantees for growing number of clusters of unbalanced sizes. Involving theoretical analysis and empirical results, we demonstrate the robustness of our algorithm against the unbalancedness of community sizes or the presence of outlier nodes.

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Jeonghwan Lee, Daesung Kim, Hye Won Chung. 2020-03-23. Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE. https://doi.org/10.1109/jsait.2020.3037170

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