| Data Augmentation for Graph Neural Networks T Zhao, Y Liu, L Neves, O Woodford, M Jiang, N Shah Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 11015 …, 2021 | 481 | 2021 |
| A unified view on graph neural networks as graph signal denoising Y Ma, X Liu, T Zhao, Y Liu, J Tang, N Shah Proceedings of the 30th ACM International Conference on Information …, 2021 | 196 | 2021 |
| Learning from Counterfactual Links for Link Prediction T Zhao, G Liu, D Wang, W Yu, M Jiang International Conference on Machine Learning, 26911-26926, 2022 | 128* | 2022 |
| Graph Data Augmentation for Graph Machine Learning: A Survey T Zhao, W Jin, Y Liu, Y Wang, G Liu, S Günnemann, N Shah, M Jiang IEEE Data Engineering Bulletin, 2023 | 126 | 2023 |
| Graph Rationalization with Environment-based Augmentations G Liu, T Zhao, J Xu, T Luo, M Jiang ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 | 99 | 2022 |
| Error-bounded graph anomaly loss for gnns T Zhao, C Deng, K Yu, T Jiang, D Wang, M Jiang Proceedings of the 29th ACM International Conference on Information …, 2020 | 71 | 2020 |
| Empowering graph representation learning with test-time graph transformation W Jin, T Zhao, J Ding, Y Liu, J Tang, N Shah International Conference on Learning Representations, 2023 | 68 | 2023 |
| Linkless Link Prediction via Relational Distillation Z Guo, W Shiao, S Zhang, Y Liu, N Chawla, N Shah, T Zhao International Conference on Machine Learning, 2023 | 61 | 2023 |
| Llaga: Large language and graph assistant R Chen, T Zhao, A Jaiswal, N Shah, Z Wang International Conference on Machine Learning, 2024 | 60 | 2024 |
| Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? H Mao, Z Chen, W Jin, H Han, Y Ma, T Zhao, N Shah, J Tang Conference on Neural Information Processing Systems, 2023 | 59 | 2023 |
| Position: Graph Foundation Models Are Already Here H Mao, Z Chen, W Tang, J Zhao, Y Ma, T Zhao, N Shah, M Galkin, J Tang International Conference on Machine Learning, 2024 | 56* | 2024 |
| A synergistic approach for graph anomaly detection with pattern mining and feature learning T Zhao, T Jiang, N Shah, M Jiang IEEE Transactions on Neural Networks and Learning Systems 33 (6), 2393-2405, 2021 | 56 | 2021 |
| The Role of "Condition" A Novel Scientific Knowledge Graph Representation and Construction Model T Jiang, T Zhao, B Qin, T Liu, NV Chawla, M Jiang Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 54 | 2019 |
| MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization X Han, T Zhao, Y Liu, X Hu, N Shah International Conference on Learning Representations, 2023 | 48 | 2023 |
| Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering M Ju, W Yu, T Zhao, C Zhang, Y Ye Empirical Methods in Natural Language Processing, 2022 | 43 | 2022 |
| Diversifying content generation for commonsense reasoning with mixture of knowledge graph experts W Yu, C Zhu, L Qin, Z Zhang, T Zhao, M Jiang Annual Meeting of the Association for Computational Linguistics, 2022 | 40 | 2022 |
| Federated dynamic gnn with secure aggregation M Jiang, T Jung, R Karl, T Zhao arXiv preprint arXiv:2009.07351, 2020 | 38 | 2020 |
| Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization M Ju, T Zhao, Q Wen, W Yu, N Shah, Y Ye, C Zhang International Conference on Learning Representations, 2023 | 35 | 2023 |
| Action Sequence Augmentation for Early Graph-based Anomaly Detection T Zhao, B Ni, W Yu, Z Guo, N Shah, M Jiang ACM International Conference on Information & Knowledge Management, 2021 | 33* | 2021 |
| Identifying referential intention with heterogeneous contexts W Yu, M Yu, T Zhao, M Jiang Proceedings of The Web Conference 2020, 962-972, 2020 | 32 | 2020 |
Neil ShahResearch Scientist at SnapVerificeret mail på snap.com