Hugging Face Trending Papers
Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships
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arXiv Machine Learning
Sep 10
Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships
The paper introduces Bayesian matrix-valued graphs (BMVG), where each edge is represented by a symmetric positive-definite matrix instead of a scalar weight, allowing the capture of direction-dependent interactions in scientific graphs. Using the affine-invariant Riemannian metric, BMVG quantifies deformation magnitude and signed directions of edge changes across contexts, and demonstrates competitive performance against existing methods in precision recovery and structural change detection. Experiments on weather data and TCGA-BRCA gene networks show that BMVG can reveal context-dependent reconfigurations in spatial coupling and gene-module interactions.
arXiv Machine Learning
5d ago
Moment-guided edge sampling
The paper introduces a moment-guided edge sampling framework that quantifies how local edge edits affect global graph structure using spectral moments of the random-walk transition matrix. Two complementary methods— a combinatorial closed‑form update for low‑order moments and a low‑rank approach exploiting locality and cyclic trace invariance— enable efficient computation of moment changes for single or batched edits. These moment changes serve as interpretable structural signatures, and preserving them is shown to retain key graph properties such as triangle‑weighted clustering, while also improving performance in supervised node classification and graph contrastive learning.
arXiv Machine Learning
Sep 16
A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction
arXiv:2604.02535v2 Announce Type: replace Abstract: Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure....
arXiv Statistics ML
3d ago
Barycentric subspace analysis of network-valued data
arXiv:2507.23559v2 Announce Type: replace-cross Abstract: Certain data are naturally modeled by networks or weighted graphs, be they biological networks or mobility networks. When there is no canonic...
arXiv Machine Learning
Jul 24
HierarchicalDAEW: Domain-Aware Edge-Weighted Graph Convolution with Evidential Uncertainty for Multi-Section Spatial Gene Expression Prediction from H&E Histology
arXiv:2607. 20896v1 Announce Type: new Abstract: Spatial transcriptomics assays remain costly and technically demanding, restricting transcriptome-wide profiling to specialist settings and preventing routine clinical deployment.
arXiv AI
Aug 6
Tropical Algebraic Geometry for Neuronal Representations: An Arakelov-Green Measure Based Descriptor for Graph Learning
arXiv:2608. 04460v1 Announce Type: cross Abstract: The quantitative analysis of 3D neuronal morphologies requires capturing both graph topology and spatial geometry.