Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE

• URL: http://arxiv.org/abs/2003.10038v3
• Date: Mon, 16 Nov 2020 04:52:06 GMT
• Title: Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE
• Authors: Jeonghwan Lee, Daesung Kim and Hye Won Chung
• Abstract summary: We study hypergraph clustering in the weighted $d$uniform hypergraph block model ($d$textsf-WHSBM) We propose a new hypergraph clustering algorithm, called textsfCRTMLE, and provide its performance guarantee under the $d$textsf-WHSBM for general parameter regimes.
• Score: 12.805268849262246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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