Researchers have developed a new methodology for directed graph learning that addresses trainable nonsymmetric propagation operators. This approach utilizes a two-sided cone Rayleigh framework to enable spectral certification, sensitivity analysis, and control without requiring symmetry or cone preservation. Numerical experiments on the Cora citation network demonstrated that adaptive recomputation of right-left sensitivity reduced a distinguished spectral level by approximately 21.5% with no impact on test accuracy. AI
IMPACT This research introduces a novel approach to directed graph learning, potentially improving spectral analysis and control in complex network models.
RANK_REASON The cluster contains an academic paper detailing a new methodology for directed graph learning.
Read on Hugging Face Daily Papers →
- alphaXiv
- CatalyzeX
- Cora
- DagsHub
- Directed Graph Learning
- Gotit.pub
- Hugging Face
- IArxiv
- Influence Flower
- Minimax Spectral Certificates
- Rayleigh Quotients
- ScienceCast
- arXiv
- Cone Extended Rayleigh Quotients
- Cora citation network
- Rayleigh framework
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →