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We investigate the enhancement of graph neural networks' (GNNs) representation power through their ability in substructure counting.
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Sign and Basis Invariant Networks for Spectral Graph Representation Learning. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning
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From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness. In International Conference on Learning Representations
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Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (, Long Beach, CA, USA,) (KDD ’23) . Association for Computing Machinery, New York, NY, USA, 237–249
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