| Exploring the potential of large language models (llms) in learning on graphs Z Chen, H Mao, H Li, W Jin, H Wen, X Wei, S Wang, D Yin, W Fan, H Liu, ... ACM SIGKDD Explorations Newsletter 25 (2), 42-61, 2024 | 73 | 2024 |
| Source Free Graph Unsupervised Domain Adaptation H Mao, L Du, Y Zheng, Q Fu, Z Li, X Chen, S Han, D Zhang Proceedings of the 17th ACM International Conference on Web Search and Data …, 2024 | 21* | 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 Advances in Neural Information Processing Systems 36, 2024 | 17 | 2024 |
| Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation W Jin*, H Mao*, Z Li, H Jiang, C Luo, H Wen, H Han, H Lu, Z Wang, R Li, ... arXiv preprint arXiv:2307.09688, 2023 | 16 | 2023 |
| Label-free node classification on graphs with large language models (llms) Z Chen, H Mao, H Wen, H Han, W Jin, H Zhang, H Liu, J Tang arXiv preprint arXiv:2310.04668, 2023 | 15 | 2023 |
| A Large Scale Search Dataset for Unbiased Learning to Rank H Mao*, L Zou*, X Chu, J Tang, W Ye, S Wang, D Yin arXiv preprint arXiv:2207.03051, 2022 | 13* | 2022 |
| Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking J Li, H Shomer, H Mao, S Zeng, Y Ma, N Shah, J Tang, D Yin Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
| Neuron Campaign for Initialization Guided by Information Bottleneck Theory H Mao, X Chen, Q Fu, L Du, S Han, D Zhang Proceedings of the 30th ACM International Conference on Information …, 2021 | 10 | 2021 |
| Alternately optimized graph neural networks H Han, X Liu, H Mao, MA Torkamani, F Shi, V Lee, J Tang International Conference on Machine Learning, 12411-12429, 2023 | 9* | 2023 |
| Revisiting link prediction: A data perspective H Mao, J Li, H Shomer, B Li, W Fan, Y Ma, T Zhao, N Shah, J Tang arXiv preprint arXiv:2310.00793, 2023 | 7 | 2023 |
| Company competition graph Y Zhang, Y Lu, H Mao, J Huang, C Zhang, X Li, R Dai arXiv preprint arXiv:2304.00323, 2023 | 6 | 2023 |
| Neuron with Steady Response Leads to Better Generalization H Mao*, Q Fu*, L Du*, X Chen, W Fang, S Han, D Zhang arXiv preprint arXiv:2111.15414, 2021 | 4* | 2021 |
| Whole Page Unbiased Learning to Rank H Mao, L Zou, Y Zheng, J Tang, X Chu, J Zhao, D Yin arXiv preprint arXiv:2210.10718, 2022 | 2 | 2022 |
| Graph Foundation Models H Mao*, Z Chen*, W Tang, J Zhao, Y Ma, T Zhao, N Shah, M Galkin, ... arXiv preprint arXiv:2402.02216, 2024 | 1 | 2024 |
| A Data Generation Perspective to the Mechanism of In-Context Learning H Mao, G Liu, Y Ma, R Wang, J Tang arXiv preprint arXiv:2402.02212, 2024 | 1 | 2024 |
| Adaptive Pairwise Encodings for Link Prediction H Shomer, Y Ma, H Mao, J Li, B Wu, J Tang arXiv preprint arXiv:2310.11009, 2023 | 1 | 2023 |
| Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark X Qian, Z Guo, J Li, H Mao, B Li, S Wang, Y Ma arXiv preprint arXiv:2403.06017, 2024 | | 2024 |
| Universal link predictor by In-context Learning K Dong, H Mao, Z Guo, NV Chawla arXiv preprint arXiv:2402.07738, 2024 | | 2024 |
| Neural Scaling Laws on Graphs J Liu, H Mao, Z Chen, T Zhao, N Shah, J Tang arXiv preprint arXiv:2402.02054, 2024 | | 2024 |
| Form 10-K Itemization RD Yanci Zhang, Mengjia Xia, Mingyang Li, Haitao Mao, Yutong Lu, Yupeng Lan ... arXiv preprint arXiv:2303.04688, 2023 | | 2023 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu
