{"total":87,"rows":[{"id":"2303.01028","title":"Specformer: Spectral Graph Neural Networks Meet Transformers","created":"2023-03-02","cited_by_count":102,"area":null},{"id":"2211.08854","title":"Graph Filters for Signal Processing and Machine Learning on Graphs","created":"2022-11-16","cited_by_count":53,"area":null},{"id":"2302.12432","title":"Graph Neural Networks with Learnable and Optimal Polynomial Bases","created":"2023-02-24","cited_by_count":36,"area":null},{"id":"2403.19852","title":"A Review of Graph Neural Networks in Epidemic Modeling","created":"2024-03-28","cited_by_count":31,"area":null},{"id":"2306.15427","title":"Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions","created":"2023-06-27","cited_by_count":28,"area":null},{"id":"2302.05631","title":"A Survey on Spectral Graph Neural Networks","created":"2023-02-11","cited_by_count":26,"area":null},{"id":"2405.12474","title":"How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing","created":"2024-05-21","cited_by_count":24,"area":null},{"id":"2310.02232","title":"HoloNets: Spectral Convolutions do extend to Directed Graphs","created":"2023-10-03","cited_by_count":20,"area":null},{"id":"2405.19121","title":"Spatio-Spectral Graph Neural Networks","created":"2024-05-29","cited_by_count":17,"area":null},{"id":"2406.03464","title":"Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach","created":"2024-06-05","cited_by_count":17,"area":"moe"},{"id":"2310.09202","title":"Graph Distillation with Eigenbasis Matching","created":"2023-10-13","cited_by_count":16,"area":null},{"id":"2410.20105","title":"FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference","created":"2024-10-26","cited_by_count":16,"area":null},{"id":"2408.09212","title":"Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier","created":"2024-08-17","cited_by_count":14,"area":"safety"},{"id":"2506.07624","title":"Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks","created":"2025-06-09","cited_by_count":12,"area":null},{"id":"2401.11772","title":"LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning","created":"2024-01-22","cited_by_count":11,"area":null},{"id":"2405.17034","title":"FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks","created":"2024-05-27","cited_by_count":11,"area":null},{"id":"2407.14459","title":"PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer","created":"2024-07-19","cited_by_count":10,"area":null},{"id":"2410.19192","title":"TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version","created":"2024-10-24","cited_by_count":10,"area":null},{"id":"2306.03624","title":"On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering","created":"2023-06-06","cited_by_count":9,"area":null},{"id":"2305.19872","title":"Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials","created":"2023-05-31","cited_by_count":8,"area":null},{"id":"2306.11132","title":"Fairness-aware Message Passing for Graph Neural Networks","created":"2023-06-19","cited_by_count":7,"area":null},{"id":"2311.06554","title":"PGODE: Towards High-quality System Dynamics Modeling","created":"2023-11-11","cited_by_count":7,"area":null},{"id":"2505.00552","title":"Graph Spectral Filtering with Chebyshev Interpolation for Recommendation","created":"2025-05-01","cited_by_count":7,"area":null},{"id":"2507.02409","title":"S2FGL: Spatial Spectral Federated Graph Learning","created":"2025-07-03","cited_by_count":7,"area":null},{"id":"2309.13599","title":"From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited","created":"2023-09-24","cited_by_count":6,"area":null},{"id":"2310.10064","title":"Shape-aware Graph Spectral Learning","created":"2023-10-16","cited_by_count":6,"area":null},{"id":"2401.11840","title":"Learning to Approximate Adaptive Kernel Convolution on Graphs","created":"2024-01-22","cited_by_count":6,"area":null},{"id":"2402.05291","title":"Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling","created":"2024-02-07","cited_by_count":6,"area":null},{"id":"2405.15218","title":"AGS-GNN: Attribute-guided Sampling for Graph Neural Networks","created":"2024-05-24","cited_by_count":6,"area":null},{"id":"2406.09675","title":"A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness","created":"2024-06-14","cited_by_count":6,"area":null},{"id":"2210.16508","title":"Clenshaw Graph Neural Networks","created":"2022-10-29","cited_by_count":5,"area":null},{"id":"2403.07954","title":"Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach","created":"2024-03-12","cited_by_count":5,"area":null},{"id":"2412.19106","title":"ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters","created":"2024-12-26","cited_by_count":5,"area":null},{"id":"2501.04570","title":"Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report","created":"2025-01-08","cited_by_count":5,"area":null},{"id":"2303.13750","title":"LON-GNN: Spectral GNNs with Learnable Orthonormal Basis","created":"2023-03-24","cited_by_count":4,"area":null},{"id":"2401.09071","title":"Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering","created":"2024-01-17","cited_by_count":4,"area":null},{"id":"2401.14876","title":"Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem","created":"2024-01-26","cited_by_count":4,"area":null},{"id":"2408.14964","title":"Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning","created":"2024-08-27","cited_by_count":4,"area":null},{"id":"2502.12618","title":"Uncertainty-Aware Graph Structure Learning","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2309.08630","title":"PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions","created":"2023-09-12","cited_by_count":3,"area":null},{"id":"2403.03676","title":"Simplified PCNet with Robustness","created":"2024-03-06","cited_by_count":3,"area":null},{"id":"2510.10864","title":"HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations","created":"2025-10-13","cited_by_count":3,"area":null},{"id":"2305.06142","title":"Feature Expansion for Graph Neural Networks","created":"2023-05-10","cited_by_count":2,"area":null},{"id":"2307.05841","title":"Influential Simplices Mining via Simplicial Convolutional Network","created":"2023-07-11","cited_by_count":2,"area":null},{"id":"2406.01249","title":"Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters","created":"2024-06-03","cited_by_count":2,"area":null},{"id":"2408.08583","title":"GrassNet: State Space Model Meets Graph Neural Network","created":"2024-08-16","cited_by_count":2,"area":null},{"id":"2412.17240","title":"Rethinking Cancer Gene Identification through Graph Anomaly Analysis","created":"2024-12-23","cited_by_count":2,"area":null},{"id":"2505.14033","title":"Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2505.20034","title":"Graph Wave Networks","created":"2025-05-26","cited_by_count":2,"area":null},{"id":"2310.11762","title":"A Quasi-Wasserstein Loss for Learning Graph Neural Networks","created":"2023-10-18","cited_by_count":1,"area":null},{"id":"2404.15354","title":"Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach","created":"2024-04-15","cited_by_count":1,"area":null},{"id":"2405.00577","title":"Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review","created":"2024-05-01","cited_by_count":1,"area":null},{"id":"2410.13373","title":"Addressing Graph Heterogeneity and Heterophily from A Spectral Perspective","created":"2024-10-17","cited_by_count":1,"area":null},{"id":"2412.11739","title":"Asymmetric Learning for Spectral Graph Neural Networks","created":"2024-12-16","cited_by_count":1,"area":null},{"id":"2502.20885","title":"A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning","created":"2025-02-28","cited_by_count":1,"area":null},{"id":"2505.04808","title":"Piecewise Constant Spectral Graph Neural Network","created":"2025-05-07","cited_by_count":1,"area":null},{"id":"2507.05640","title":"Learnable quantum spectral filters for hybrid graph neural networks","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2605.05759","title":"Full-Spectrum Graph Neural Networks: Expressive and Scalable","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2606.12494","title":"Net-Ev$^2$: A Generative Simulator for Network Event Evolution","created":"2026-06-10","cited_by_count":1,"area":null},{"id":"2403.16358","title":"ChebMixer: Efficient Graph Representation Learning with MLP Mixer","created":"2024-03-25","cited_by_count":0,"area":null},{"id":"2404.10353","title":"Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis","created":"2024-04-16","cited_by_count":0,"area":null},{"id":"2408.13661","title":"Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models","created":"2024-08-24","cited_by_count":0,"area":null},{"id":"2408.15255","title":"Emotion Classification from Multi-Channel EEG Signals Using HiSTN: A Hierarchical Graph-based Spatial-Temporal Approach","created":"2024-08-09","cited_by_count":0,"area":null},{"id":"2412.10789","title":"Scaling Up Graph Propagation Computation on Large Graphs: A Local Chebyshev Approximation Approach","created":"2024-12-14","cited_by_count":0,"area":null},{"id":"2501.10454","title":"Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture","created":"2025-01-14","cited_by_count":0,"area":null},{"id":"2502.05724","title":"Rethinking Link Prediction for Directed Graphs","created":"2025-02-08","cited_by_count":0,"area":null},{"id":"2502.16483","title":"A Split-Window Transformer for Multi-Model Sequence Spammer Detection using Multi-Model Variational Autoencoder","created":"2025-02-23","cited_by_count":0,"area":null},{"id":"2504.02248","title":"CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection","created":"2025-04-03","cited_by_count":0,"area":null},{"id":"2504.14250","title":"Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection","created":"2025-04-19","cited_by_count":0,"area":null},{"id":"2508.11328","title":"Aligning the Spectrum: Hybrid Graph Pre-training and Prompt Tuning across Homophily and Heterophily","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2508.11497","title":"Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2509.02803","title":"A Graph Laplacian Eigenvector-based Pre-training Method for Graph Neural Networks","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2509.10337","title":"Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2510.22928","title":"Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond","created":"2025-10-27","cited_by_count":0,"area":"diffusion"},{"id":"2512.13228","title":"ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2603.08137","title":"Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach","created":"2026-03-09","cited_by_count":0,"area":null},{"id":"2605.10975","title":"Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.12699","title":"Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.16809","title":"Informative Graph Structure Learning","created":"2026-05-16","cited_by_count":0,"area":null},{"id":"2605.21247","title":"Graph Navier Stokes Networks","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2605.23467","title":"S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.01660","title":"Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.15346","title":"DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2607.18332","title":"ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction","created":"2026-07-19","cited_by_count":0,"area":null},{"id":"2609.28979","title":"Spectral Graph Neural Networks with Hermite Polynomials: A Comprehensive Study","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.33674","title":"Reachability is not enough: Diagnosing long-range behavior in GNNs","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.34729","title":"Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}