تقرير
Permutation Equivariant Generative Adversarial Networks for Graphs
| العنوان: | Permutation Equivariant Generative Adversarial Networks for Graphs |
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| المؤلفون: | Boget, Yoann, Gregorova, Magda, Kalousis, Alexandros |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | One of the most discussed issues in graph generative modeling is the ordering of the representation. One solution consists of using equivariant generative functions, which ensure the ordering invariance. After having discussed some properties of such functions, we propose 3G-GAN, a 3-stages model relying on GANs and equivariant functions. The model is still under development. However, we present some encouraging exploratory experiments and discuss the issues still to be addressed. Comment: ELLIS Machine Learning for Molecule Discovery Workshop. 5 pages + ref. + appendix |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2112.03621 |
| رقم الأكسشن: | edsarx.2112.03621 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |