Hierarchical and Unsupervised Graph Representation Learning with Loukas’s Coarsening
We propose a novel algorithm for unsupervised graph representation learning with attributed graphs. It combines three advantages addressing some current limitations of the literature: (i) The model is inductive: it can embed new graphs without re-training in the presence of new data; (ii) The method...
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| Главные авторы: | , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
MDPI AG
2020-08-01
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| Серии: | Algorithms |
| Предметы: | |
| Online-ссылка: | https://www.mdpi.com/1999-4893/13/9/206 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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