On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph. In many real-world applications, however, features are only partially available; for example, in...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Proceedings of Machine Learning Research
2022
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