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Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks.

, , , , and . ICDM, page 1287-1292. IEEE, (2022)

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Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models., , , , , , and . IEEE BigData, page 1214-1225. IEEE, (2021)Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks., , , , and . ICDM, page 1287-1292. IEEE, (2022)GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data., , , , , and . KDD, page 772-782. ACM, (2019)Fast Flow-based Random Walk with Restart in a Multi-query Setting., , , and . SDM, page 342-350. SIAM, (2018)Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks., , , , and . CoRR, (2021)Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices., , , , and . CoRR, (2021)Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective., , and . CoRR, (2023)Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs., , , , , and . NeurIPS, (2020)Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks., , , , and . KDD, page 1223-1234. ACM, (2023)A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks., , , , and . 3DV, page 1331-1340. IEEE, (2021)