| Autor: |
Dong, Yihe, Sawin, Will, Bengio, Yoshua |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Graph Representation Learning and Beyond Workshop at ICML 2020 |
| Druh dokumentu: |
Working Paper |
| Popis: |
Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges, combined with a normalization scheme that can flexibly adjust the importance of high-cardinality hyperedges and high-degree vertices depending on the dataset. We demonstrate improved performance of HNHN in both classification accuracy and speed on real world datasets when compared to state of the art methods. |
| Databáze: |
arXiv |
| Externí odkaz: |
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