Spectral Graph Convolution

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Inverse Cross-spectral Neural Networks for Multivariate Time Series
Oct 5, 2026Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto +2Graph Neural Networks Spectral Graph Convolution
Statistical Sciences Dept., Sapienza University of Rome, Italy · DIET Dept., Sapienza University of Rome, Italy
Spectral Graph Neural Networks with Hermite Polynomials: A Comprehensive Study
Sep 24, 2026Shuang WuGraph Neural Networks Spectral Graph Convolution
UCLA
LiftGCN: Efficient Energy-Preserving Graph Learning via Joukowski Spectral Lifting for Finite Element Stress Prediction
Sep 14, 2026Chen Zeng, Qiao WangGraph Neural Networks Spectral Graph Convolution
School of Information Science and Engineering, Southeast University · School of Economics and Management, Southeast University
Nonlinear Laplacians Improve Signed-Directed Graph Learning
Aug 1, 2026Ali Parviz, Yuichi YoshidaGraph Neural Networks Spectral Graph Convolution
Mila - Quebec AI Institute · New Jersey Institute of Technology · National Institute of Informatics (NII) +1
Dynamic Spectral Filtering for Temporal Graph Learning: Learning Evolving Propagation Operators
Jul 30, 2026Yan KongTemporal GNNs Temporal Link Prediction
Nanjing University of Information Science and Technology, China
Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection
Jun 26, 2026Liming Liu, Chao Hu, Mingfei Lu +3Graph Neural Networks Graph SSL
Central South University, Changsha, China · University of Technology Sydney, Australian Artificial Intelligence Institute, Sydney, Australia
Convex--Concave Quadratic Spectral Filtering for Graph Neural Networks
Jun 23, 2026Ranhui Yan, Jia Cai, Mengzhu Chen +1Graph Neural Networks Spectral Methods
School of Computing and Artificial Intelligence, Guangzhou Xinhua University, Guangzhou, Guangdong, China · School of Statistics and Data Science, Guangdong University of Finance and Economics,2026 Guangzhou, Guangdong, China
A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
Jun 2, 2026Ankang Yang, Jitao Zhao, Dongxiao He +3Graph Neural Networks Cross-Domain Generalization
School of Computer Science and Technology Tianjin University Tianjin, China · School of Artificial Intelligence Hebei University of Technology Tianjin, China · Department of Health Technology and Informatics Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Kowloon, Hong Kong
Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks
May 26, 2026Lihui Liu, Mucun Sun, Caisheng WangRL Control Graph Neural Networks
Wayne State University, Detroit, MI, USA
Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
May 13, 2026Eden Nagar, Ya-Wei Eileen Lin, Ron LevieGraph Neural Networks Spectral Graph Convolution
Technion - Israel Institute of Technology, Faculty of Mathematics · Technical University of Munich, School of Computation, Information and Technology · Munich Center for Machine Learning
Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
May 8, 2026Md Sazzad Hossen, Avimanyu SahooGraph Neural Networks Over-Squashing
University of Alabama in Huntsville, Huntsville, AL, USA.
Full-Spectrum Graph Neural Networks: Expressive and Scalable
May 7, 2026Xiaohan Wang, Deyu Bo, Longlong Li +1Graph Neural Networks GNN Expressivity
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.
Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans
Oct 12, 2025Theo Di Piazza, Carole Lazarus, Olivier Nempont +1Graph Neural Networks Graph Representation Learning
INSA Lyon, University of Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Villeurbanne, France · Hospices Civil de Lyon, Lyon, France · Philips Clinical Informatics, Innovation Paris, France

Oct 5, 2026, stat.MLJ/K move · Enter open · S save

Inverse Cross-spectral Neural Networks for Multivariate Time Series

Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto, Paolo Di Lorenzo+1

Statistical Sciences Dept., Sapienza University of Rome, Italy · DIET Dept., Sapienza University of Rome, Italy

Graph Neural Networks Spectral Graph Convolution

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CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators directly from second-order statistics. These architectures, however, are designed for independent and identically distributed observations and do not fully capture the joint structure of temporal and cross-variable dependencies in multivariate time series. In this work, we introduce Inverse Cross-Spectral Neural Networks (iCSNNs), a class of graph neural networks for stationary multivariate time series whose shift operators are the inverse cross-spectral density (iCSD) matrices. These operators encode frequency-specific conditional relationships among variables, exploiting the decomposition provided by the spectral representation theorem. Leveraging spectral smoothness, frequencies are grouped into bands sharing a single iCSD operator, yielding a compact parametrisation that retains the frequency-dependent structure of the process. We further propose a joint learning procedure to estimate both the Fourier-domain dependence structure and the iCSNN parameters, adapting the iCSD operators to the downstream task. When tested on synthetic data, iCSNN outperforms baselines from different methodological families.

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