| 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 | 262 | 2024 |
| 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 | 58 | 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 International Conference on Learning Representations, 2024 | 55 | 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 | 51 | 2024 |
| Graph foundation models 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, 2024 | 42* | 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, ... Advances in neural information processing systems 36, 2023 | 37 | 2023 |
| 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 | 32 | 2024 |
| A Large Scale Search Dataset for Unbiased Learning to Rank H Mao*, L Zou*, X Chu, J Tang, W Ye, S Wang, D Yin Advances in Neural Information Processing Systems 35, 2022 | 22 | 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 International Conference on Learning Representations, 2024 | 21 | 2024 |
| 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 | 19 | 2024 |
| Neural scaling laws on graphs J Liu, H Mao, Z Chen, T Zhao, N Shah, J Tang Learning on Graphs Conference 2024, 2024 | 13 | 2024 |
| 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 | 13 | 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 | 12 | 2021 |
| Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights Z Chen, H Mao, J Liu, Y Song, B Li, W Jin, B Fatemi, A Tsitsulin, B Perozzi, ... Advances in neural information processing systems 37, 2024 | 6 | 2024 |
| A data generation perspective to the mechanism of in-context learning H Mao, G Liu, Y Ma, R Wang, K Johnson, J Tang arXiv preprint arXiv:2402.02212, 2024 | 6 | 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 |
| Lpformer: An adaptive graph transformer for link prediction H Shomer, Y Ma, H Mao, J Li, B Wu, J Tang Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 4* | 2024 |
| 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 Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 4 | 2024 |
| PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming B Li, L Yang, Y Chen, S Wang, Q Chen, H Mao, Y Ma, A Wang, T Ding, ... International Conference on Machine Learning, 2024 | 4 | 2024 |
| 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 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu
