Currently, Xunkai Li is a third year Ph.D. student of School of Computer Science in Beijing Institute of Technology supervised by Prof. Rong-Hua Li. Prior to that, he received his B.Eng. degree in computer science department from Shandong University in June 2022.

Since 2021, he worked with Prof. Wentao Zhang as a research intern at the Data-centric Machine Learning (DCML) group in the Center of Machine Learning Research at Peking University (PKU). As an active member of the team, Xunkai is the contributor of scalable graph learning system project SGL , federated graph learning system project OpenFGL , and graph unlearning system project OpenGU .

Email: cs.xunkai.li@gmail.com

Wechat (微信): 18045124943

🌟 🌟 If you are interested in collaborating with me or want to have a chat, always feel free to contact me through e-mail or WeChat ：）

My research interests center on Data-Centric Graph Intelligence, including Data-centric AI, Graph Machine Learning, and AI4Science, with a focus on developing efficient ML solutions for structured data representation and mining. The goal of my work is to bridge the gap between theoretical advancements and practical applications in real-world industrial and scientific scenarios.

Data-centric AI: In recent years, the development of AI has encountered certain challenges, with many leading LLMs still being based on the Transformer. Consequently, research priorities are increasingly shifting from model-centric approaches to data-centric paradigms. As a result, the pivotal role of data in driving AI advancements has become more pronounced, particularly in Data-centric LLM Optimization.

Graph Machine Learning: Graphs are pervasive across various domains, such as social networks (relationships), biomedicine (interactions), and recommendation systems (preferences). Graph ML, especially Graph Neural Networks (GNNs), not only provides more effective solutions but also breaks through conventional methods, offering a novel perspective. Currently, I am particularly interested in Graph ML in the era of LLM.

AI4Science: The potential of AI4Science lies in its capacity to revolutionize our understanding and transformation of the world, with profound implications across all aspects of human life. My goal is to advance the practical application of artificial intelligence in scientific domains, driving a new wave of innovation—particularly in Integrating Graph and LLMs into Scientific Researches.

To date, I have published papers in leading international conferences on machine learning, databases, data mining, and artificial intelligence, including ICML, VLDB, ICDE, WWW, AAAI, and IJCAI. An overview of my research roadmap is as follows:

• Data Quantity: Annotation [arXiv 2025 OGA], Synthesize [arXiv 2025 GraphMaster], and Large-scale (Scalability) [WWW 2024 ATP] [VLDB 2024 LightDiC] [arXiv 2025 ScaDyG], Stream.

• Data Quality: Paradigm (Directed, Hypergraph, Heterophily, Temporal, and Multimodal) [TKDE 2023 AHGAE] [ICDE 2024 ADPA] [WWW 2025 MAP] and Augmentation (Imbalance, Sparsity, Noise, and Out-Of-Distribution) [arXiv 2025 UltraTAG] [arXiv 2025 LLaTA] [arXiv 2025 LRW-OOD].

• Data Efficiency: Distillation (Coarsening, Sparsification, Condensation, and Pruning) [VLDB 2025 GEC] [ICML 2025 EDEN], Generalization (Graph Foundation Model) [arXiv 2025 MoT].

• Data Privacy: Federated [VLDB 2023 FedGTA] [IJCAI 2024 FedTAD] [ICDE 2024 AdaFGL] [IJCAI 2025 FedGM] [VLDB 2025 OpenFGL] [arXiv 2025 FedPG] [arXiv 2025 FairFGL] [arXiv 2025 FedGKC] [arXiv 2025 FedC4] [arXiv 2025 FedGFM] and Unlearning [AAAI 2024 MEGU] [arXiv 2025 SGU] [arXiv 2025 OpenGU].

• Data Management: Collection, Organization, and Benchmark.

• Data Application: LLM Optimization (RAG, Reasoning, Multimodal, and Agent System) and AI4Science (AI4Protein, AI4Healthcare, and AI4Social).

🔥 News

• 2025-05: One paper is accepted by ICML 2025.
• 2025-04: One paper is accepted by IJCAI 2025.
• 2025-01: One paper is accepted by TKDE 2025.
• 2025-01: One paper is accepted by WWW 2025.
• 2025-01: One paper is accepted by VLDB 2025.
• 2024-08: One paper is accepted by VLDB 2025.
• 2024-04: One paper is accepted by IJCAI 2024.
• 2024-03: One paper is accepted by ICDE 2024.
• 2024-02: One paper is accepted by VLDB 2024.
• 2024-01: One paper is accepted by WWW 2024.
• 2023-12: One paper is accepted by AAAI 2024.
• 2023-10: One paper is accepted by ICDE 2024.
• 2023-08: One paper is accepted by VLDB 2023.

📝 Publications

First/Co-first Author

# indicates equal contribution

ICML 2025

🎯 Toward Data-centric Directed Graph Learning: An Entropy-driven Approach, [Code]

Xunkai Li, Zhengyu Wu, Kaichi Yu, Hongchao Qin, Guang Zeng, Rong-Hua Li, Guoren Wang

• International Conference on Machine Learning (ICML), 2025, CCF-A.

WWW 2025

🎯 Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach, [Code]

Xunkai Li, Daohan Su, Zhengyu Wu, Guang Zeng, Hongchao Qin, Rong-Hua Li, Guoren Wang

• The Web Conference (WWW), 2025, CCF-A.

VLDB 2025

🎯 OpenFGL: A Comprehensive Benchmark for Federated Graph Learning, [Code]

Xunkai Li, Yinlin Zhu, Boyang Pang, Guochen Yan, Yeyu Yan, Zening Li, Zhengyu Wu, Wentao Zhang, Rong-Hua Li, Guoren Wang

• International Conference on Very Large Data Bases (VLDB), 2025, CCF-A.

ICDE 2024

🎯 AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity, [Code]

Xunkai Li, Zhenyu Wu, Wentao Zhang, Henan Sun, Rong-Hua Li, Gouren Wang

• IEEE International Conference on Data Engineering (ICDE), 2024, CCF-A.

AAAI 2024

🎯 Towards Effective and General Graph Unlearning via Mutual Evolution, [Code]

Xunkai Li#, Yulin Zhao#, Zhengyu Wu, Wentao Zhang, Rong-Hua Li, Gouren Wang

• Association for the Advancement of Artificial Intelligence (AAAI), 2024, CCF-A.
• 🎉🎉 Oral Presentation

WWW 2024

🎯 Rethinking Node-wise Propagation for Large-scale Graph Learning, [Code]

Xunkai Li, Jingyuan Ma, Zhengyu Wu, Daohan Su, Wentao Zhang, Rong-Hua Li, Guoren Wang

• The Web Conference (WWW), 2024, CCF-A.
• 🎉🎉 Oral Presentation

VLDB 2024

🎯 LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning, [Code]

Xunkai Li, Meihao Liao, Zhengyu Wu, Daohan Su, Wentao Zhang, Rong-Hua Li, Guoren Wang

• International Conference on Very Large Data Bases (VLDB), 2024, CCF-A.

ICDE 2024

🎯 Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification, [Code]

Henan Sun#, Xunkai Li#, Zhengyu Wu, Daohan Su, Rong-Hua Li, Gouren Wang

• IEEE International Conference on Data Engineering (ICDE), 2024, CCF-A.

VLDB 2023

🎯 FedGTA: Topology-aware Averaging for Federated Graph Learning, [Code]

Xunkai Li, Zhenyu Wu, Wentao Zhang, Yinlin Zhu, Rong-Hua Li, Guoren Wang

• International Conference on Very Large Data Bases (VLDB), 2023, CCF-A.

NCAA 2023

Effective Hybrid Graph and Hypergraph Convolution Network for Collaborative Filtering [code]

Xunkai Li#, Ronghui Guo#, Jianwen Chen, Youpeng Hu, Meixia Qu, Bin Jiang

• Neural Computing & Applications (NCAA) 2023, CCF-C.

Co-author

IJCAI 2025

🎯 Rethinking Federated Graph Learning: A Data Condensation Perspective, [Code]

Hao Zhang, Xunkai Li, Yinlin Zhu, Lianglin Hu

• International Joint Conference on Artificial Intelligence (IJCAI), 2025, CCF-A.

TKDE 2025

🎯 Acceleration Algorithms in GNNs: A Survey

Lu Ma, Zeang Sheng, Xunkai Li, Xinyi Gao, Zhezheng Hao, Ling Yang, Wentao Zhang, Bin Cui

• IEEE Transactions on Knowledge and Data Engineering (TKDE) 2025, CCF-A.

VLDB 2025

🎯 Topology-preserving Graph Coarsening: An Elementary Collapse-based Approach, [Code]

Yuchen Meng, Rong-Hua Li, Longlong Lin, Xunkai Li, Gouren Wang

• International Conference on Very Large Data Bases (VLDB), 2025, CCF-A.

IJCAI 2024

🎯 FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning, [Code]

Yinlin Zhu, Xunkai Li, Zhengyu Wu, Di Wu, Miao Hu, Rong-Hua Li

• International Joint Conference on Artificial Intelligence (IJCAI), 2024, CCF-A.

TKDE 2023

🎯 Adaptive Hypergraph Auto-Encoder for Relational Data Clustering [code]

Youpeng Hu, Xunkai Li, Yujie Wang, Yixuan Wu, Yining Zhao, Chenggang Yan, Jian Yin, Yue Gao

• IEEE Transactions on Knowledge and Data Engineering (TKDE) 2023, CCF-A.

🎯 FedPPD: Towards Effective Subgraph Federated Learning via Pseudo Prototype Distillation

Qi Lin, Jishuo Jia, Yinlin Zhu, Xunkai Li, Bin Jiang, Meixia Qu

• Neural Networks (NN) 2025, CCF-B.

🎯 LoyalDE: Improving The Performance of Graph Neural Networks with Loyal Node Discovery and Emphasis

Haotong Wei, Yinlin Zhu, Xunkai Li, Bin Jiang

• Neural Networks (NN) 2023, CCF-B.

🎯 Handling Information Loss of Graph Convolutional Networks in Collaborative Filtering

Xin Xiong, Xunkai Li, Youpeng Hu, Yixuan Wu, Jian Yin

• Knowledge and Information Systems (IS) 2022, CCF-B.

🎯 Graph Relation Embedding Network for Click-through Rate Prediction

Yixuan Wu, Youpeng Hu, Xin Xiong, Xunkai Li, Ronghui Guo, Shuiguang Deng

• Knowledge and Information Systems (KAIS) 2022, CCF-B.

📖 Educations

• 2022.09 - 2028.06 (expected), Ph.D. in Computer Science, Beijing Institute of Technology (BIT)

• 2018.09 - 2022.06, B. Eng in Computer Science, Shandong University (SDU)

💬 Invited Talks

• 2024.08, Oral presentation at IJCAI 2024 about our paper: ‘FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning’.
• 2024.05, Oral presentation at WWW 2024 about our paper: ‘Rethinking Node-wise Propagation for Large-scale Graph Learning’.
• 2024.05, Oral presentation at ICDE 2024 about our paper: ‘Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification’ and ‘AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity’.
• 2024.02, Oral presentation at AAAI 2024 about our paper: ‘Towards Effective and General Graph Unlearning via Mutual Evolution’.

🧑‍💻 Service

• Conference Reviewers:
  • ICML, ICLR, NeurIPS, KDD, WWW, AISTATS, AAAI, IJCAI
• Journal Reviewers:
  • TKDE, TKDD, TNNLS, TBD

💻 Internships

• Research Intern
  • Company/Institution: The Hong Kong University of Science and Technology, Guangzhou.
  • Advisor: Prof. Jia Li
  • Employment period: From 3/2025 to the present
• Research Intern
  • Company/Institution: Ant Group, Alibaba, Beijing.
  • Advisor: Researcher Guang Zeng
  • Employment period: From 12/2024 to the present
• Research Intern
  • Company/Institution: Large Language Model Center, IAAR, Shanghai.
  • Advisor: Dr. Zhiyu Li
  • Employment period: From 06/2024 to the present
• Research Intern
  • Company/Institution: DCML Group, Peking University
  • Advisor: Prof. Wentao Zhang
  • Employment period: From 10/2021 to the present
• Research Intern
  • Company/Institution: iMoon Lab, Tsinghua University
  • Advisor: Prof. Yue Gao
  • Employment period: From 06/2020 to 06/2021