Understanding and extending subgraph GNNs by rethinking their symmetries
Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN architectures as well as their basic theoretical properties are still largely unexplored. In this paper, we study the most pro...
| 主要な著者: | , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
Curran Associates
2022
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