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arXiv Machine Learning By Zhongyuan Lyu, Ting Li, Dong Xia

Optimal Clustering of Multi-layer Networks under Mixture Block Models

Read the original on arXiv Machine Learning →

The paper investigates clustering of multi‑layer networks generated under a mixture multi‑layer stochastic block model (MMSBM). It derives the minimax optimal clustering error rate, expressed via Chernoff information between component edge‑probability distributions, and introduces a two‑stage estimator that achieves this rate even under extreme sparsity. The method is extended to count‑weighted networks using a mixture multi‑layer Poisson block model, with numerical experiments and real‑data analyses demonstrating its practical performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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