| Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs J Zhu, Y Yan, L Zhao, M Heimann, L Akoglu, D Koutra (NeurIPS 2020) Advances in Neural Information Processing Systems 33, 2020 | 1060 | 2020 |
| Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks Y Yan, M Hashemi, K Swersky, Y Yang, D Koutra 2022 IEEE International Conference on Data Mining (ICDM), 1287-1292, 2022 | 310 | 2022 |
| Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models Z Zhang, Z Yao, Y Yang, Y Yan, JE Gonzalez, MW Mahoney (BigData 2021) IEEE International Conference on Big Data (regular paper), 2020 | 125* | 2020 |
| GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data Y Yan, J Zhu, M Duda, E Solarz, C Sripada, D Koutra (KDD 2019, ORAL) Proceedings of the 25th ACM SIGKDD International Conference …, 2019 | 101 | 2019 |
| Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices P Trivedi, ES Lubana, Y Yan, Y Yang, D Koutra (WWW 2022) The Web Conference 2022, 2021 | 53 | 2021 |
| Neural execution engines: Learning to execute subroutines Y Yan, K Swersky, D Koutra, P Ranganathan, M Hashemi (NeurIPS 2020) Advances in Neural Information Processing Systems 33, 2020 | 48 | 2020 |
| Heterophily and Graph Neural Networks: Past, Present and Future J Zhu, Y Yan, M Heimann, L Zhao, L Akoglu, D Koutra Data Engineering, 10, 2023 | 14 | 2023 |
| Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks G Li, M Duda, X Zhang, D Koutra, Y Yan (KDD 2023) Proceedings of the 29th ACM SIGKDD International Conference on …, 2023 | 10 | 2023 |
| Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective Y Yan, G Li, D Koutra arXiv preprint arXiv:2305.15611, 2023 | 6 | 2023 |
| Fast flow-based random walk with restart in a multi-query setting Y Yan, M Heimann, D Jin, D Koutra (SDM 2018) Proceedings of the 2018 SIAM International Conference on Data …, 2018 | 6 | 2018 |
| Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification Y Wang, N Huang, T Li, Y Yan, X Zhang NeurIPS 2024, 2024 | 4 | 2024 |
| Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning Z Huang, Q Yang, D Zhou, Y Yan (ICML 2024) Forty-first International Conference on Machine Learning, 2024 | 2 | 2024 |
| A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks Y Zhou, Y Shen, Y Yan, C Feng, Y Yang (3DV 2021) The 9th International Conference on 3D Vision, 2021 | 2 | 2021 |
| EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs H Wang, Y Mao, Y Yan, Y Yang, J Sun, K Choi, B Veeramani, A Hu, ... (ICML 2024) Forty-first International Conference on Machine Learning, 2024 | 1* | 2024 |
| How to evaluate your medical time series classification? Y Wang, T Li, Y Yan, W Song, X Zhang arXiv preprint arXiv:2410.03057, 2024 | | 2024 |
| GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs V Gupta, X Chen, R Huang, F Meng, J Chen, Y Yan (CIKM 2024) 33rd ACM International Conference on Information and Knowledge …, 2024 | | 2024 |
| Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance H Lu, X Liu, Y Zhou, Q Li, K Keutzer, MW Mahoney, Y Yan, H Yang, ... NeurIPS 2024, 2024 | | 2024 |
| Evaluating the Structural Awareness of Large Language Models on Graphs: Can They Count Substructures? L Nguyen, Y Yan KDD Undergraduate Consortium, 2024 | | 2024 |
| Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks. X Liu, Y Cai, Q Yang, Y Yan NeurIPS 2024, 2024 | | 2024 |
| Towards Generalizable Neural Networks for Graph Applications Y Yan | | 2022 |
Danai KoutraUniversity of Michigan, Ann ArborVerified email at umich.edu
