A machine learning based computer-aided molecular design/screening methodology for fragrance molecules. - Lei Zhang 040; Haitao Mao; Linlin Liu; Jian Du 003; Rafiqul Gani (Comput. Chem. Eng., 2018)

Neuron Campaign for Initialization Guided by Information Bottleneck Theory. - Haitao Mao; Xu Chen 022; Qiang Fu 015; Lun Du; Shi Han; Dongmei Zhang 001 (CIKM, 2021)

Source Free Unsupervised Graph Domain Adaptation. - Haitao Mao; Lun Du; Yujia Zheng 001; Qiang Fu 015; Zelin Li 001; Xu Chen 022; Shi Han; Dongmei Zhang 001 (CoRR, 2021)

Neuron with Steady Response Leads to Better Generalization. - Qiang Fu 015; Lun Du; Haitao Mao; Xu Chen 022; Wei Fang 005; Shi Han; Dongmei Zhang 001 (NeurIPS, 2022)

A Large Scale Search Dataset for Unbiased Learning to Rank. - Lixin Zou; Haitao Mao; Xiaokai Chu; Jiliang Tang; Wenwen Ye; Shuaiqiang Wang; Dawei Yin (NeurIPS, 2022)

Whole Page Unbiased Learning to Rank. - Haitao Mao; Lixin Zou; Yujia Zheng 001; Jiliang Tang; Xiaokai Chu; Jiashu Zhao; Dawei Yin 005 (CoRR, 2022)

Alternately Optimized Graph Neural Networks. - Haoyu Han 001; Xiaorui Liu; Haitao Mao; MohamadAli Torkamani; Feng Shi; Victor Lee; Jiliang Tang (ICML, 2023)

Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation. - Wei Jin 009; Haitao Mao; Zheng Li 018; Haoming Jiang; Chen Luo 003; Hongzhi Wen; Haoyu Han 001; Hanqing Lu; Zhengyang Wang; Ruirui Li 002; Zhen Li; Monica Xiao Cheng; et al. (NeurIPS, 2023)

Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking. - Juanhui Li; Harry Shomer; Haitao Mao; Shenglai Zeng; Yao Ma 001; Neil Shah; Jiliang Tang; Dawei Yin 001 (NeurIPS, 2023)

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? - Haitao Mao; Zhikai Chen; Wei Jin 009; Haoyu Han 001; Yao Ma 001; Tong Zhao 003; Neil Shah; Jiliang Tang (NeurIPS, 2023)

Form 10-K Itemization. - Yanci Zhang; Mengjia Xia; Mingyang Li; Haitao Mao; Yutong Lu; Yupeng Lan; Jinlin Ye; Rui Dai (CoRR, 2023)

Adaptive Pairwise Encodings for Link Prediction. - Harry Shomer; Yao Ma 001; Haitao Mao; Juanhui Li; Bo Wu 002; Jiliang Tang (CoRR, 2023)

Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs. - Zhikai Chen; Haitao Mao; Hang Li 007; Wei Jin 009; Hongzhi Wen; Xiaochi Wei; Shuaiqiang Wang; Dawei Yin 001; Wenqi Fan; Hui Liu 031; Jiliang Tang (SIGKDD Explor., 2023)

Intrinsic Self-correction for Enhanced Morality: An Analysis of Internal Mechanisms and the Superficial Hypothesis. - Guangliang Liu; Haitao Mao; Jiliang Tang; Kristen Marie Johnson (EMNLP, 2024)

Label-free Node Classification on Graphs with Large Language Models (LLMs). - Zhikai Chen; Haitao Mao; Hongzhi Wen; Haoyu Han 001; Wei Jin 009; Haiyang Zhang; Hui Liu 031; Jiliang Tang (ICLR, 2024)

Revisiting Link Prediction: a data perspective. - Haitao Mao; Juanhui Li; Harry Shomer; Bingheng Li; Wenqi Fan; Yao Ma 001; Tong Zhao 003; Neil Shah; Jiliang Tang (ICLR, 2024)

PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming. - Bingheng Li; Linxin Yang; Yupeng Chen; Senmiao Wang; Haitao Mao; Qian Chen; Yao Ma 001; Akang Wang; Tian Ding; Jiliang Tang; Ruoyu Sun 001 (ICML, 2024)

Position: Graph Foundation Models Are Already Here. - Haitao Mao; Zhikai Chen; Wenzhuo Tang; Jianan Zhao 002; Yao Ma 001; Tong Zhao 003; Neil Shah; Mikhail Galkin 001; Jiliang Tang (ICML, 2024)

Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark. - Xiaowei Qian; Zhimeng Guo; Jialiang Li 003; Haitao Mao; Bingheng Li; Suhang Wang; Yao Ma 001 (KDD, 2024)

LPFormer: An Adaptive Graph Transformer for Link Prediction. - Harry Shomer; Yao Ma 001; Haitao Mao; Juanhui Li; Bo Wu 002; Jiliang Tang (KDD, 2024)

Towards Neural Scaling Laws on Graphs. - Jingzhe Liu; Haitao Mao; Zhikai Chen; Tong Zhao 003; Neil Shah; Jiliang Tang (LoG, 2024)

Do Neural Scaling Laws Exist on Graph Self-Supervised Learning? - Qian Ma 012; Haitao Mao; Jingzhe Liu; Zhehua Zhang; Chunlin Feng; Yu Song; Yihan Shao; Yao Ma 001 (LoG, 2024)

A Pure Transformer Pretraining Framework on Text-Attributed Graphs. - Yu Song 007; Haitao Mao; Jiachen Xiao; Jingzhe Liu; Zhikai Chen; Wei Jin 009; Carl Yang 001; Jiliang Tang; Hui Liu 031 (LoG, 2024)

Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights. - Zhikai Chen; Haitao Mao; Jingzhe Liu; Yu Song 007; Bingheng Li; Wei Jin 009; Bahare Fatemi; Anton Tsitsulin; Bryan Perozzi; Hui Liu 031; Jiliang Tang (NeurIPS, 2024)

Source Free Graph Unsupervised Domain Adaptation. - Haitao Mao; Lun Du; Yujia Zheng 001; Qiang Fu 015; Zelin Li 001; Xu Chen 022; Shi Han; Dongmei Zhang 001 (WSDM, 2024)

The 1st International Workshop on Graph Foundation Models (GFM). - Haitao Mao; Jianan Zhao 002; Xiaoxin He; Zhikai Chen; Qian Huang; Zhaocheng Zhu; Jian Tang 005; Michael M. Bronstein; Xavier Bresson; Bryan Hooi; Haiyang Zhang; Xianfeng Tang; et al. (WWW (Companion Volume), 2024)

Whole Page Unbiased Learning to Rank. - Haitao Mao; Lixin Zou; Yujia Zheng 001; Jiliang Tang; Xiaokai Chu; Jiashu Zhao; Qian Wang 002; Dawei Yin 005 (WWW, 2024)

Neural Scaling Laws on Graphs. - Jingzhe Liu; Haitao Mao; Zhikai Chen; Tong Zhao 003; Neil Shah; Jiliang Tang (CoRR, 2024)

A Data Generation Perspective to the Mechanism of In-Context Learning. - Haitao Mao; Guangliang Liu; Yao Ma 001; Rongrong Wang; Jiliang Tang (CoRR, 2024)

Graph Foundation Models. - Haitao Mao; Zhikai Chen; Wenzhuo Tang; Jianan Zhao 002; Yao Ma 001; Tong Zhao 003; Neil Shah; Mikhail Galkin 001; Jiliang Tang (CoRR, 2024)

Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models. - Wenzhuo Tang; Haitao Mao; Danial Dervovic; Ivan Brugere; Saumitra Mishra; Yuying Xie 001; Jiliang Tang (CoRR, 2024)

On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept. - Guangliang Liu; Haitao Mao; Bochuan Cao; Zhiyu Xue; Kristen Marie Johnson; Jiliang Tang; Rongrong Wang (CoRR, 2024)

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs. - Jingzhe Liu; Haitao Mao; Zhikai Chen; Wenqi Fan; Mingxuan Ju; Tong Zhao 003; Neil Shah; Jiliang Tang (CoRR, 2024)

Research on Network Teaching Collaboration Platform Using Flipped Classroom Teaching Mode. - Jing Zhao; Haitao Mao; Panpan Mao; Junyong Hao; Meige Mao (J. Inf. Knowl. Manag., 2024)

Impact assessment and mechanism of water conservancy policy on carbon emission performance under the background of artificial intelligence. - Junjie Xiang; Haitao Mao; Bin Yang (Expert Syst. J. Knowl. Eng., 2024)

A Survey to Recent Progress Towards Understanding In-Context Learning. - Haitao Mao; Guangliang Liu; Yao Ma 001; Rongrong Wang; Kristen Marie Johnson; Jiliang Tang (NAACL (Findings), 2025)

Universal Link Predictor By In-Context Learning on Graphs. - Kaiwen Dong; Haitao Mao; Zhichun Guo; Nitesh V. Chawla (Trans. Mach. Learn. Res., 2025)

On the Convergence of Moral Self-Correction in Large Language Models. - Guangliang Liu; Haitao Mao; Bochuan Cao; Zhiyu Xue; Xitong Zhang; Rongrong Wang; Kristen Marie Johnson (CoRR, 2025)

Graph Machine Learning in the Era of Large Language Models (LLMs). - Shijie Wang 002; Jiani Huang 001; Zhikai Chen; Yu Song 007; Wenzhuo Tang; Haitao Mao; Wenqi Fan; Hui Liu 031; Xiaorui Liu; Dawei Yin 001; Qing Li 001 (ACM Trans. Intell. Syst. Technol., 2025)