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A Survey on The Expressive Power of Graph Neural Networks
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Graph neural networks (GNNs) are effective machine learning models for various graph learning problems. Despite their empirical successes, the theoretical limitations of GNNs have been revealed recently. Consequently, many GNN models have been proposed to overcome these limitations. In this survey, we provide a comprehensive overview of the expressive power of GNNs and provably powerful variants of GNNs.
Forward citations
Cited by 2 Pith papers
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Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
Under a fixed leakage-free protocol, NC→LP transfer reliably helps on homophilic graphs while LP→NC helps mainly when LP is easy and NC is unsaturated; homophily and CoTask Score guide mechanism choice.
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On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks
Excess risk of SGD and ridge regression on GNNs is characterized through graph spectra, showing graph shape decides which algorithm generalizes better and deeper networks amplify the difference.
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