Jiong Zhu
Jiong Zhu
Applied Scientist, Amazon
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Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
J Zhu, Y Yan, L Zhao, M Heimann, L Akoglu, D Koutra
Advances in Neural Information Processing Systems 33, 2020
Graph neural networks with heterophily
J Zhu, RA Rossi, A Rao, T Mai, N Lipka, NK Ahmed, D Koutra
Proceedings of the AAAI conference on artificial intelligence 35 (12), 11168 …, 2021
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
Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019
How does heterophily impact the robustness of graph neural networks? theoretical connections and practical implications
J Zhu, J Jin, D Loveland, MT Schaub, D Koutra
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
On performance discrepancies across local homophily levels in graph neural networks
D Loveland, J Zhu, M Heimann, B Fish, MT Schaub, D Koutra
Learning on Graphs Conference, 6: 1-6: 30, 2024
Heterophily and graph neural networks: Past, present and future
J Zhu, Y Yan, M Heimann, L Zhao, L Akoglu, D Koutra
IEEE Data Engineering Bulletin, 2023
Graph coarsening via convolution matching for scalable graph neural network training
C Dickens, E Huang, A Reganti, J Zhu, K Subbian, D Koutra
Companion Proceedings of the ACM on Web Conference 2024, 1502-1510, 2024
Simplifying distributed neural network training on massive graphs: Randomized partitions improve model aggregation
J Zhu, A Reganti, E Huang, C Dickens, N Rao, K Subbian, D Koutra
arXiv preprint arXiv:2305.09887, 2023
Graph neural networks for datasets with heterophily
R Rossi, T Mai, N Lipka, J Zhu, A Rao, V Swaminathan
US Patent App. 17/210,157, 2022
The 3rd Workshop on Graph Learning Benchmarks (GLB 2023)
J Ma, J Zhu, Y Dong, D Koutra, J He, Q Mei, A Tsitsulin, X Zhang, M Zitnik
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023
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