arXiv (All)
open access
Spectral Graph Neural Networks with Hermite Polynomials: A Comprehensive Study
We study spectral graph neural networks built from Hermite polynomials and propose HermNet, a simple model that combines a nodewise predictor with normalized Hermite propagation. Its sparse recurrence requires neither eigendecomposition nor a learned basis. We distinguish the basic model from option
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Spectrally symmetric orientations of graphs#997458
- Learning to Pre-train Graph Neural Networks#120633
- Learning Latent Graph Geometry via Fixed-Point Schrödinger-Type Activation: A Theoretical Study#984058
- Preconditioned Physics-Informed Neural Operator Training#1131058
- Continuous-Time Quantum Walks based Graph Neural Network#659292
Record · ID 1076635
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive