Cover Image

Resolving Node Identifiability in Graph Neural Processes via Laplacian Spectral Encodings

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yan, Zimo, Xie, Zheng, Liu, Chang, Wang, Yuan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bridging Distance and Spectral Positional Encodings via Anchor-Based Diffusion Geometry Approximation
by: Yan, Zimo, et al.
Published: (2026)

Chernoff Bounds for Tensor Expanders on Riemannian Manifolds Using Graph Laplacian Approximation
by: Chang, Shih-Yu
Published: (2024)

Renormalized Graph Representations for Node Classification
by: Caso, Francesco, et al.
Published: (2023)

Permutation-Invariant Spectral Learning via Dyson Diffusion
by: Schwarz, Tassilo, et al.
Published: (2025)

Neural Diffusion Intensity Models for Point Process Data
by: Du, Xinlong, et al.
Published: (2026)

Random ReLU Neural Networks as Non-Gaussian Processes
by: Parhi, Rahul, et al.
Published: (2024)

Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
by: Wang, Hai-Xiao, et al.
Published: (2024)

Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
by: Zhang, Rui, et al.
Published: (2023)

A Multi-Scale Graph Neural Process with Cross-Drug Co-Attention for Drug-Drug Interactions Prediction
by: Yan, Zimo, et al.
Published: (2025)

Neural Continuous-Time Markov Chain: Discrete Diffusion via Decoupled Jump Timing and Direction
by: Li, Jingyuan, et al.
Published: (2026)

MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
by: Yan, Zimo, et al.
Published: (2025)

Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architectures
by: Lu, Weiguo, et al.
Published: (2025)

Asymptotic Gaussian Fluctuations of Eigenvectors in Spectral Clustering
by: Lebeau, Hugo, et al.
Published: (2024)

Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle
by: Giovagnini, Filippo, et al.
Published: (2026)

Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing
by: Zhang, Yihan, et al.
Published: (2023)

Strategic A/B testing via Maximum Probability-driven Two-armed Bandit
by: Zhang, Yu, et al.
Published: (2025)

Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
by: Platonov, Oleg, et al.
Published: (2022)

Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
by: Labarthe, Aldric
Published: (2026)

Gaussian Equivalence for Self-Attention: Asymptotic Spectral Analysis of Attention Matrix
by: Hayase, Tomohiro, et al.
Published: (2025)

Spectral alignment of stochastic gradient descent for high-dimensional classification tasks
by: Arous, Gerard Ben, et al.
Published: (2023)

Global Dynamics of Heavy-Tailed SGDs in Nonconvex Loss Landscape: Characterization and Control
by: Wang, Xingyu, et al.
Published: (2025)

Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains
by: Doshi, Vishwaraj, et al.
Published: (2023)

Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
by: Wang, Yu, et al.
Published: (2026)

Diffusion Processes on Implicit Manifolds
by: Kawasaki-Borruat, Victor, et al.
Published: (2026)

Obstacle-aware Gaussian Process Regression
by: Shrivastava, Gaurav
Published: (2024)

Multi-Community Spectral Clustering for Geometric Graphs
by: Allem, Luiz Emilio, et al.
Published: (2025)

Reconstructing the Geometry of Random Geometric Graphs
by: Huang, Han, et al.
Published: (2024)

Temporal Graph Neural Networks for Early Anomaly Detection and Performance Prediction via PV System Monitoring Data
by: Mukherjee, Srijani, et al.
Published: (2025)

Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting
by: Leng, Yan, et al.
Published: (2026)

BN-Pool: Bayesian Nonparametric Pooling for Graphs
by: Castellana, Daniele, et al.
Published: (2025)

Importance Corrected Neural JKO Sampling
by: Hertrich, Johannes, et al.
Published: (2024)

Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
by: Wald, Christian, et al.
Published: (2025)

Sampling non-log-concave densities via Hessian-free high-resolution dynamics
by: Wang, Xiaoyu, et al.
Published: (2026)

Wasserstein-p Central Limit Theorem Rates: From Local Dependence to Markov Chains
by: Zhang, Yixuan, et al.
Published: (2026)

Nonparametric Identification of Latent Concepts
by: Zheng, Yujia, et al.
Published: (2025)

A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
by: Noack, Marcus M., et al.
Published: (2023)

Machine learning-based system reliability analysis with Gaussian Process Regression
by: Zhou, Lisang, et al.
Published: (2024)

Spectral Structure in Finite Free Information Inequalities and $p$-Stam Phase Transitions
by: Hashemi, Baran
Published: (2026)

Static and Dynamic Approaches to Computing Barycenters of Probability Measures on Graphs
by: Gentile, David, et al.
Published: (2026)

Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling
by: Benigni, Lucas, et al.
Published: (2025)