A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data
Summary
RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference in high-dimensional data, supporting partial correlation networks and conditional Gaussian Bayesian networks with graphlet-based topology analysis.
View Cached Full Text
Cached at: 05/14/26, 06:17 AM
# A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data Source: [https://arxiv.org/abs/2605.12706](https://arxiv.org/abs/2605.12706) [View PDF](https://arxiv.org/pdf/2605.12706) > Abstract:RSNet is an open\-source R package that provides a resampling\-based framework for robust and interpretable network inference, designed to address the limited\-sample\-size challenges common in high\-dimensional data\. It supports both the estimation of partial correlation networks modeled as Gaussian networks and conditional Gaussian Bayesian networks for mixed data types that combine continuous and discrete variables\. The framework incorporates multiple resampling strategies, including bootstrap, subsampling, and cluster\-based approaches, to accommodate both independent and correlated observations\. To enhance interpretability, RSNet integrates graphlet\-based topology analysis that captures higher\-order connectivity and edge sign information, enabling single\-node and subnetwork\-level insights\. Notably, RSNet is the first R package to efficiently construct signed graphlet degree vector matrices \(GDVMs\) in near\-constant time for sparse networks, providing scalable analysis of higher\-order network structure\. Collectively, RSNet offers a versatile tool for statistically reliable and interpretable network inference in high\-dimensional data\. ## Submission history From: Stefano Monti \[[view email](https://arxiv.org/show-email/6ba3184d/2605.12706)\] **\[v1\]**Tue, 12 May 2026 20:08:46 UTC \(272 KB\)
Similar Articles
Geometry-Aware R-Structured Kolmogorov-Arnold Networks
Proposes Geometry-aware R-Structured KAN (GRS-KAN), a hybrid neural architecture that integrates R-functions into KAN to encode geometric and logical constraints, achieving up to 67% RMSE reduction on regression benchmarks with discontinuities.
Schreier-Coset Graph Rewiring
Introduces Schreier-Coset Graph Rewiring, a group-theoretic method to rewire graphs for GNNs, mitigating over-squashing by improving spectral gap and effective resistance. Empirical results show significant reduction in effective resistance across learning tasks.
Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models
Researchers propose a lightweight autoregressive framework for graph generation that uses structure-guided topological ordering to achieve near log-linear complexity, addressing scalability and novelty limitations of existing diffusion and autoregressive methods. The approach supports both LSTM and Mamba-style backbones and shows improved novelty while maintaining validity and uniqueness on molecular and non-molecular benchmarks.
A Local Sinkhorn Framework for Conditional Distribution Reconstruction of Multidimensional Random Fields
This paper proposes a scalable local Sinkhorn divergence framework for training stochastic neural networks to reconstruct multidimensional random fields, with theoretical generalization error bounds and numerical demonstrations for uncertainty quantification.
Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
This paper introduces HMH, a hierarchical multi-scale Graph Neural Network framework designed to address oversmoothing and oversquashing in heterophilous graphs. It utilizes spectral filters with Haar bases to achieve scalable learning and improved performance on node and graph classification tasks.