Publications

Underline indicates student mentored. *represents equal contribution. Co-first authors in name alphabetical order.

LLM mechanism (and toy NN)

• A Data Generation Perspective to the Mechanism of In-Context Learning
  Haitao Mao, Guangliang Liu, Yao Ma, Rongrong Wang, Jiliang Tang
  preprint [pdf]

• Neuron Campaign for Initialization Guided by Information Bottleneck Theory
  Haitao Mao*, Lun Du*, Qiang Fu*, Xu Chen*, Shi Han, Dongmei Zhang
  CIKM2021 Best Short Paper
  Work during internship in Microsoft Research Asia
  [pdf] [github] [blog] [Chinese blog] [Offical Poster] [Offical Slides] [Offical Video] [AI TIME Introduction] [AI TIME presentation Slides] [AI TIME Presentation Video] [AI TIME Report]

• Neuron with Steady Response Leads to Better Generalization
  Haitao Mao*, Lun Du*, Qiang Fu*, Xu Chen, Wei Fang, Shi Han, Dongmei Zhang
  NeurIPS2022
  Work during internship in Microsoft Research Asia
  [pdf] [code] [Offical video] [Official slides] [Long presentation video] [Long presentation slides] [Poster] [Microsoft Research Asia News]

Graph mechanism towards Graph Foundation Models

• Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
  Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang
  NeurIPS 2023
  collaboration with SnapChat
  [pdf] [code] [slides] [poster] [video]

• Revisiting Link Prediction: A data perspective
  Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang
  ICLR 2024
  collaboration with SnapChat
  [pdf] [slides] [video]

• Graph Foundation Models
  Haitao Mao*, Zhikai Chen*, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang
  ICML 2024
  collaboration with SnapChat and Intel
  [pdf] [blog] [reading List 1] [reading List 2]

• Neural Scaling Law on Graph
  Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang
  collaboration with SnapChat
  preprint [pdf] [blog]

• Universal Link Predictor by In-Context Learning on Graphs
  Kaiwen Dong, Haitao Mao, Zhichun Guo, Nitesh Chewla
  preprint [pdf]

New Graph applications

Graph Structure in the User Behavior

• Whole Page Unbiased Learning to Rank
  Haitao Mao, Lixin Zou, Yujia Zheng, Jiliang Tang, Xiaokai Chu, Jiashu Zhao, Dawei Yin
  WebConference 2024 Oral [pdf]
  Work during internship in Baidu
  

Privacy-preserve Graph structure learning

• Source Free Graph Unsupervised Domain Adaptation
  Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, Dongmei Zhang
  WSDM 2024 Best Paper Honor Mention
  Work during internship in Microsoft Research Asia
  [pdf] [blog] [code]

Graph Structure in Leaning to Optimize

• PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
  Bingheng Li, Linxin Yang, Yupeng Chen, Senmiao Wang, Qian Chen, Haitao Mao, Yao Ma, Akang Wang, Tian Ding, Jiliang Tang, Ruoyu Sun
  ICML 2024
  [pdf]

Graph Structure in the Finance Domain

• Company Competition Graph
  Yanci Zhang, Yutong Lu, Haitao Mao, Jiawei Huang, Cien Zhang, Xinyi Li, Rui Dai
  MAF 2024
  collaboration with Wharton Data Center
  [pdf]
  

• Form 10-K Itemization
  Yanci Zhang, Mengjia Xia, Mingyang Li, Haitao Mao, Yutong Lu, Yupeng Lan, Jinlin Ye, Rui Dai
  collaboration with Wharton Data Center
  preprint [pdf]

Benchmarking

• Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
  Juanhui Li*, Harry Shomer*, Haitao Mao, Shenglai Zeng, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin
  NeurIPS 2023 Dataset Track
  collaboration with SnapChat
  [pdf] [code] [poster]

• Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
  Xiaowei Qian, Zhimeng Guo, Jialiang Li, Haitao Mao, Bingheng Li, Suhang Wang, Yao Ma
  preprint [pdf][code]

New LLM applications in Data Mining

• A Large Scale Search Dataset for Unbiased Learning to Rank
  Haitao Mao*, Lixin Zou*, Xiaokai Chu, Jiliang Tang, Shuaiqiang Wang, Wenwen ye, Dawei yin.
  NeurIPS 2022 Dataset Track
  Work during internship in Baidu
  [pdf] [Code1] [Code2] [Dataset Homepage1] [Dataset Homepage2] [Long presentation video] [Long presentation slides] [Poster]

• Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
  Wei Jin*, Haitao Mao*, Zheng Li, Haoming Jiang, Chen Luo, Hongzhi Wen, Haoyu Han, Hanqing Lu, Zhengyang Wang, Ruirui Li, Zhen Li, Monica Cheng, Rahul Goutam, Haiyang Zhang, Karthik Subbian, Suhang Wang, Yizhou Sun, Jiliang Tang, Bing Yin, and Xianfeng Tang
  NeurIPS 2022 Dataset Track
  collaboration with Amazon
  [pdf] [homepage] [instructions] [code] [poster]

• Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
  Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang
  SIGKDD Explorations 2023
  collaboration with Baidu
  [pdf] [code] [slides]

• Label-free Node Classification on Graphs with Large Language Models (LLMS)
  Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang
  ICLR 2024
  collaboration with Amazon
  [pdf] [code] [slides]

• Graph Machine Learning in the Era of Large Language Models (LLMs)
  Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang,
  Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
  [pdf]

Others

• AI4Science101: Graph Machine Learning
  Haitao Mao, Yuanqi Du, Yanbang Wang
  [blog page]

• LPFormer: An Adaptive Graph Transformer for Link Prediction
  Harry Shomer, Yao Ma, Haitao Mao, Juanhui Li, Bo Wu, Jiliang Tang
  preprint [pdf] [code]

• Alternately Optimized Graph Neural Network
  Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi, Victor Lee, Jiliang Tang
  collaboration with TigerGraph
  ICML 2023
  [pdf] [code]