Publications

Students with bold-font names are primarily advised by me when doing the corresponding project.

Selected Publications:

The following are the papers I like most. A more complete list of the publications that I mostly joined is here.

• Graphs and Large Language Models

• Haoyu Wang, Peihao Wang, Mufei Li, Shikun Liu, Siqi Miao, Zhangyang Wang, Pan Li, Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models (code) NeurIPS 2025

• Haoyu Wang, Shikun Liu, Rongzhe Wei, Pan Li, Generalization Principles for Inference over Text-Attributed Graphs with Large Language Models (code) ICML 2025

• Jiajun Zhu, Peihao Wang, Ruisi Cai, Jason D. Lee, Pan Li, Zhangyang Wang, Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding (code) ICML 2025

• Mufei Li, Siqi Miao, Pan Li, Simple is effective: The roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation (codes) ICLR 2025

• Graph ML Foundation

• Shikun Liu, Deyu Zou, Nima Shoghi, Victor Fung, Kai Liu, Pan Li, RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models (code) NeurIPS 2025 (spotlight)

• Yinan Huang, Haoyu Wang, Pan Li, What Are Good Positional Encodings for Directed Graphs? ICLR 2025 (codes)

• Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li, "On the Stability of Expressive Positional Encodings for Graphs, " ICLR 2024 (codes)

• Peihao Wang, Shenghao Yang, Shu Li, Zhangyang Wang, Pan Li, "Polynomial Width is Sufficient for Set Representation with High-dimensional Features, " ICLR 2024

• Haorui Wang, Haoteng Yin, Muhan Zhang, Pan Li, "Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks," ICLR 2022. (code)

• Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, Long Jin, "Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning," NeurIPS 2021.(code) 

• Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, Pan Li, "Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks," ICLR 2021.  (codes) 

• Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec, "Distance Encoding -- Design Provably More Powerful GNNs for Structural Representation Learning," NeurIPS 2020. (codes)(slides)

• Trustworthy ML 

• Rongzhe Wei, Peizhi Niu, Hans Hao-Hsun Hsu, Ruihan Wu, Haoteng Yin, Yifan Li, Eli Chien, Kamalika Chaudhuri, Olgica Milenkovic, Pan Li, Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness (code) NeurIPS 2025

• Yinan Huang, Haoteng Yin, Eli Chien, Rongzhe Wei, Pan Li, Differentially Private Relational Learning with Entity-level Privacy Guarantees (code) NeurIPS 2025

• Rongzhe Wei, Mufei Li, Mohsen Ghassemi, Eleonora Kreacic, Yifan Li, Xiang Yue, Bo Li, Vamsi K. Potluru, Pan Li, Eli Chien, Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning (code) ICML 2025

• Eli Chien, Pan Li, Convergent privacy loss of noisy-sgd without convexity and smoothness ICLR 2025 

• Eli Chien, Haoyu Wang, Ziang Chen, Pan Li, Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning (Spotlight), NeurIPS, 2024

• Shikun Liu, Deyu Zou, Han Zhao, Pan Li, Pairwise Alignment Improves Graph Domain Adaptation, ICML 2024 (codes, spotlight)

• Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, Pan Li, "Structural Re-weighting Improves Graph Domain Adaptation, " ICML 2023.  (codes) 

• Tailin Wu*, Hongyu Ren*, Pan Li, Jure Leskovec, "Graph Information Bottleneck," NeurIPS 2020. (codes) (slides)

• AI for Science 

• Jiajun Zhu*, Siqi Miao*, Rex Ying, Pan Li, Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning Nature Machine Intelligence 

• Deyu Zou, Shikun Liu, Siqi Miao, Victor Fung, Shiyu Chang, Pan Li, GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shift (Benchmark Track), NeurIPS, 2024 (codes)

• Siqi Miao, Zhiyuan Lu, Mia Liu, Javier Duarte, Pan Li, Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics, ICML 2024 (codes, oral)

• Siqi Miao, Yunan Luo, Mia Liu, Pan Li, "Interpretable Geometric Deep Learning via Learnable Randomness Injection,"  ICLR 2023. (codes)

• Siqi Miao,  Miaoyuan Liu, Pan Li, "Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism," ICML 2022 (codes) 

• Tianchun Li*, Shikun Liu*, Yongbin Feng*, Garyfallia Paspalaki , Nhan V. Tran, Miaoyuan Liu , Pan Li, "Semi-supervised Graph Neural Network for Particle-level Noise Removal," The European Physics Journal C 2023, a short version appeared at NeurIPS AI4Science workshop 2021. 

• Graph Computation

Mufei Li, Viraj Shitole, Eli Chien, Changhai Man, Zhaodong Wang, Srinivas Sridharan, Ying Zhang, Tushar Krishna, Pan Li, LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation ICLR 2025 (codes) (Spotlight)

• Rongzhe Wei, Eli Chien, Pan Li, Differentially Private Graph Diffusion with Applications in Personalized PageRanks, NeurIPS, 2024

• Yuhong Luo, Pan Li, "Neighborhood-aware Scalable Temporal Network Representation Learning," LoG 2022 (best paper award!) (codes) (talks) 

• Haoteng Yin, Muhan Zhang,  Yanbang Wang, Jianguo Wang, Pan Li, "Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning," VLDB 2022 (codes)

• Pan Li and Olgica Milenkovic, "Submodular Hypergraph: p-Laplacian, Cheeger Inequalities and Spectral Clustering," ICML 2018. (slides, slides (PhD prelim), codes, poster, full-version) --- Spectral Theory for Hypergraphs!

• Pan Li and Olgica Milenkovic, "Inhomogoenous Hypergraph Clustering with Applications," NeurIPS 2017 (spotlight presentation, acceptance rate < 4.6%). (slides, slides (PhD prelim), codes, poster, full-version) --- Applications motivate the line of research for Hypergraphs and preliminary spectral theory!

Book Chapter 

• Pan Li, Jure Leskovec, "The Expressive Power of Graph Neural Networks, " Chapter 5 in "Graph Neural Networks: Foundations, Frontiers, and Applications" edited by Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang. (Also see the formal book link here: Graph Neural Networks: Foundations, Frontiers, and Applications | SpringerLink)

• Eli Chien, Mufei Li, Anthony Aportela, Kerr Ding, Shuyi Jia, Supriyo Maji, Zhongyuan Zhao, Javier Duarte, Victor Fung, Callie Hao, Yunan Luo, Olgica Milenkovic, David Pan, Santiago Segarra, Pan Li, "Exploring the Opportunities and Challenges of Graph Neural Networks in Electrical Engineering," in Nature Reviews Electrical Engineering, 2024