تقرير
Simplifying GNN Performance with Low Rank Kernel Models
| العنوان: | Simplifying GNN Performance with Low Rank Kernel Models |
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| المؤلفون: | Vinas, Luciano, Amini, Arash A. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | We revisit recent spectral GNN approaches to semi-supervised node classification (SSNC). We posit that many of the current GNN architectures may be over-engineered. Instead, simpler, traditional methods from nonparametric estimation, applied in the spectral domain, could replace many deep-learning inspired GNN designs. These conventional techniques appear to be well suited for a variety of graph types reaching state-of-the-art performance on many of the common SSNC benchmarks. Additionally, we show that recent performance improvements in GNN approaches may be partially attributed to shifts in evaluation conventions. Lastly, an ablative study is conducted on the various hyperparameters associated with GNN spectral filtering techniques. Code available at: https://github.com/lucianoAvinas/lowrank-gnn-kernels |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.05250 |
| رقم الأكسشن: | edsarx.2310.05250 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |