Author

S. Olhede

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Preprint Sep 2026

nethist: An R package for Nonparametric Graphon Estimation via Network Histograms

Understanding the generative mechanism of real-world networks is crucial for analyzing connection patterns and making inference from network data. Graphons are widely used to model such mechanisms. Network histogram methods are nonparametric approaches based on blockmodel approximations that provide an intuitive view o...

Youngseok Song, S. Olhede · 0 citations

Preprint Aug 2026

Joint Estimation of Sparse Multilayer Networks via Graph Limits

A nonparametric joint estimator based on blockmodel approximations is developed, which captures each layer's varying sparsity and connection structure, accounting for heterogeneity via shared latent variables across all layers, and enables high-resolution estimation even in sparser layers.

Youngseok Song, S. Olhede · 1 citation

Preprint Jul 2026

Decorated graphons for temporal network estimation

The framework gives a nonparametric baseline for dynamic network analysis with explicit convergence guarantees and establishes nonparametric convergence rates in both block-model and Holder-smooth regimes.

Charles Dufour, S. Olhede · 0 citations