| 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 | 157 | 2024 |
| 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 | 33 | 2023 |
| 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 | 28 | 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 | 26 | 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 | 24 | 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 | 24 | 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 | 16* | 2022 |
| 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 | 13 | 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 Forty-first International Conference on Machine Learning, 0 | 11* | |
| 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 | 10 | 2023 |
| 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 |
| Graph machine learning in the era of large language models (llms) W Fan, S Wang, J Huang, Z Chen, Y Song, W Tang, H Mao, H Liu, X Liu, ... arXiv preprint arXiv:2404.14928, 2024 | 7 | 2024 |
| Neural scaling laws on graphs J Liu, H Mao, Z Chen, T Zhao, N Shah, J Tang arXiv preprint arXiv:2402.02054, 2024 | 7 | 2024 |
| Company competition graph Y Zhang, Y Lu, H Mao, J Huang, C Zhang, X Li, R Dai arXiv preprint arXiv:2304.00323, 2023 | 5 | 2023 |
| Whole Page Unbiased Learning to Rank H Mao, L Zou, Y Zheng, J Tang, X Chu, J Zhao, Q Wang, D Yin Proceedings of the ACM on Web Conference 2024, 1431-1440, 2024 | 4 | 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 | 4 | 2024 |
| 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 |
| Universal link predictor by In-context Learning K Dong, H Mao, Z Guo, NV Chawla arXiv preprint arXiv:2402.07738, 2024 | 3 | 2024 |
| Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models W Tang, H Mao, D Dervovic, I Brugere, S Mishra, Y Xie, J Tang arXiv preprint arXiv:2406.01899, 2024 | 1 | 2024 |
| On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept G Liu, H Mao, B Cao, Z Xue, K Johnson, J Tang, R Wang arXiv preprint arXiv:2406.02378, 2024 | 1 | 2024 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu