Personal Profile

Currently, Yeyu Yan is is a third year Ph.D. student of the Institute of Information Science in Beijing Jiaotong University, supervised by Prof. Zhenfeng Zhu and Prof. Shuai Zheng. Prior to that, he received his M.S. and B.E. degrees in the School of Electronics and Information Engineering from Shandong University of Science and Technology, supervised by Prof. Chao Li and Prof. Zhongying Zhao, with graduation years of 2023 and 2020, respectively.

📧 E-mail: yanyeyu-work@foxmail.com 🔗Github: https://github.com/SuperYeYu

🎓 Research Interests

My research interests center on Data-centric Machine Learning (DCML), including Graph Machine Learning and Data-empowered AI.

• Graph Machine Learning: Currently, graph neural networks have continued to adopt Transformer or Message Passing architectures, with performance gains shifting from model improvements to data improvements. My primary focus is on obtaining large quantities of high-quality data at lower costs and in shorter timeframes, by approaching from the perspectives of graph quality, quantity, and efficiency. The main research areas are: Heterogeneous Graph Neural Networks, Self-supervised Graph Learning, Federated Graph Learning, Graph Condensation, and Graph Foundation Models.

• Data-empowered AI: As the limitations of graph data gradually become apparent, this research theme will transition from the study of graph data to the study of general data, by introducing graph concepts to enhance the collaborative efficiency between models, modules, or agents. Through graphs, LLMs or agents can be explicitly associated, further unleashing their expressive capabilities. The main research areas are: Data Valuation, Data Distillation, LLM Quantization, Model Merging, and Multi-Agent System.

🎯 Recent Focus

➤ Graph-Centric Machine Learning (GCML)

Currently, I am collaborating with my long-term partner Xiangkai Zhu to advance the systematic research on GCML. This work encompasses richer domain datasets, wider evaluation metrics, more comprehensive surveys, more systematic benchmarks, and deeper insights Since 2024, we have compiled over 140+ survey and baseline papers in the GNN field! Project Link

➤ Data Valuation in Machine Learning (DVML)

I am currently pushing forward a survey on Data Valuation With the paradigm shift from model-centric to data-centric machine learning, evaluating the value of individual or groups of data samples has become increasingly crucial. It provides excellent explainability while playing a vital role in enhancing model trustworthiness and performance. We have compiled over 160+ papers on data valuation spanning the past 10 years! Paper and Project

🔥 News (from 2025 to present)

• 2026-07: One paper is accepted by TPAMI journal.

• 2026-06: One paper is accepted by TASL journal.

• 2026-06: One paper is accepted by PR journal.

• 2026-05: One paper is accepted by PR journal.

• 2026-05: One paper is accepted by KDD conference.

• 2026-04: One paper is accepted by ICML conference.

• 2026-01: One paper is accepted by WWW conference.

• 2025-12: One paper is accepted by TNNLS journal.

• 2025-11: One paper is accepted by AAAI conference.

• 2025-10: One paper is accepted by TNSE journal.

• 2025-09: One paper is accepted by NeurIPS conference.

• 2025-06: One paper is accepted by INS journal.

• 2025-06: One paper is accepted by PR journal.

• 2025-04: One paper is accepted by IJCAI conference.

• 2025-02: One paper is accepted by VLDB conference.

📑 Selected Publications

1. Aligning Condensed Graph via Hashing: A New Insight for Federated Graph Learning
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026
   Yeyu Yan, Shuai Zheng, Zhenfeng Zhu, Dong Chen, Wentao Zhang, Yawei Zhao, Kunlun He, Yao Zhao

2. HarmoFGL: Harmonizing GNN Latent Factors for Federated Graph Learning
   IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2026
   Yeyu Yan, Zhenfeng Zhu, Shuai Zheng, Hongli Xu, Yawei Zhao, Kunlun He, Yao Zhao

3. Towards Pre-trained Graph Condensation via Optimal Transport
   Neural Information Processing Systems (NeurIPS), 2025
   Funding was provided by the Chinese Institute of Electronics
   Yeyu Yan, Shuai Zheng, Wenjun Hui, Xiangkai Zhu, Dong Chen, Zhenfeng Zhu, Yao Zhao, Kunlun He

4. A Fast and Robust Attention-free Heterogeneous Graph Convolutional Network
   IEEE Transactions on Big Data (IEEE TBD), 2024
   Yeyu Yan, Zhongying Zhao, Zhan Yang, Yanwei Yu, Chao Li

5. OSGNN: Original graph and subgraph aggregated graph neural network
   Expert Systems with Applications (ESWA), 2023
   Yeyu Yan, Chao Li, Yanwei Yu, Xiangju Li, Zhongying Zhao

6. HetReGAT-FC: Heterogeneous residual graph attention network via feature completion
   Information Sciences (INS), 2023
   Chao Li (Advisor), Yeyu Yan, Jinhu Fu, Zhongying Zhao, Qingtian Zeng

7. HEPre: Click frequency prediction of applications based on heterogeneous information network embedding
   Journal of Intelligent & Fuzzy Systems (JIFS), 2021
   Chao Li (Advisor), Yeyu Yan, Zhongying Zhao, Jun Luo, Qingtian Zeng

📜 Other Publications

1. AceQuant: An Activation-Centric Framework for High-Fidelity Low-Bit Quantization of LLMs
   IEEE Transactions on Audio, Speech and Language Processing (TASL), 2026
   Mingrun Wei, Yeyu Yan, Dong Wang
2. Evolution-Consistent Dynamic Graph Condensation
   Pattern Recognition (PR), 2026
   Dong Chen, Shuai Zheng, Yeyu Yan, Muhao Xu, Yao Zhao
3. Multi-Level Decoupled Trend Learning for GNN-Based Multivariate Time Series Prediction
   Pattern Recognition (PR), 2026
   Shaohan Li, Zhenfeng Zhu, Youru Li, Yeyu Yan, Shuai Zheng, Pengyuan Li, Yan Zhuang, Yao Zhao
4. FedRAHi: Reliability-Aware Hierarchical Collaboration for Federated Graph Foundation Models
   KDD, 2026
   Xiangkai Zhu, Yeyu Yan, Pengpeng Qiao, Tingrui Pei, Yanchun Li, Saiqin Long
5. Subspace-Aware Feature Reshaping for Open-Set Graph Class-Incremental Learning
   ICML, 2026
   Weichao Zhang, Shuai Zheng, Yeyu Yan, Zhizhe Liu, Zhenfeng Zhu, Yao Zhao
6. Mitigating Dynamic Graph Distribution Shifts via Mixture of Variational Experts
   WWW, 2026
   Qianyu Song, Chao Li, Yeyu Yan, Hui Zhou, Zhongying Zhao, Qingtian Zeng
7. Stage-Aware Graph Contrastive Learning with Node-oriented Mixture of Experts
   AAAI, 2026
   Xiangkai Zhu, Yeyu Yan, Saiqin Long, Chao Li, Longsheng Su, Guanwen Chen
8. MPPQ: Enhancing Post-Training Quantization for LLMs via Mixed Supervision, Proxy Rounding, and Pre-Searching
   IJCAI, 2025
   Mingrun Wei, Yeyu Yan, Dong Wang
9. OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
   VLDB, 2025
   Xunkai Li, Yinlin Zhu, Boyang Pang, Guochen Yan, Yeyu Yan, Zening Li, Zhengyu Wu, Wentao Zhang, Rong-Hua Li, Guoren Wang
10. Adaptive Graph Filtering Neural Network for Graph Anomaly Detection
    IEEE Transactions on Network Science and Engineering (TNSE), 2025
    Zhizhe Liu, Shuai Zheng, Yeyu Yan, Zhenfeng Zhu, Yao Zhao
11. NodeHGAE: Node-oriented Heterogeneous Graph Autoencoder
    Information Sciences (INS), 2025
    Xiangkai Zhu, Chao Li, Yeyu Yan, Zhongying Zhao, Hua Duan, Qingtian Zeng
12. Efficiently Harmonizing Information Sharing for Heterogeneous Graph Contrastive Learning
    Pattern Recognition (PR), 2025
    Xiangkai Zhu, Chao Li, Yeyu Yan, Jinhu Fu, Zhongying Zhao, Qingtian Zeng
13. MHGNN: Multi-view fusion based heterogeneous graph neural network
    Applied Intelligence (APIN), 2024
    Chao Li, Xiangkai Zhu, Yeyu Yan, Zhongying Zhao, Lingtao Su, Qingtian Zeng
14. Higher order heterogeneous graph neural network based on node attribute enhancement
    Expert Systems with Applications (ESWA), 2024
    Chao Li, Jinhu Fu, Yeyu Yan, Zhongying Zhao, Qingtian Zeng
15. HetGNN-SF: Self-supervised learning on heterogeneous graph neural network via semantic strength and feature similarity
    Applied Intelligence (APIN), 2023
    Chao Li, Xinming Liu, Yeyu Yan, Zhongying Zhao, Qingtian Zeng
16. Self-Supervised Heterogeneous Graph Neural Network Model Based on Collaborative Contrastive Learning of Topology Information and Attribute Information
    Pattern Recognition and Artificial Intelligence, 2023
    Chao Li, Guoyi Sun, Yeyu Yan, Hua Duan, Qingtian Zeng

📌 Service

• Conference Reviewers:
  • ICDE 2024, ICML 2024, IJCAI 2025, AAAI 2026, CVPR 2026, ECCV 2026, ICML 2026, NeurIPS 2026, AAAI 2027, KDD 2027
• Journal Reviewers:
  • Transactions on Neural Networks and Learning Systems (TNNLS), Transactions on Machine Learning Research (TMLR), Transactions on Knowledge Discovery from Data (TKDD), Pattern Recognition (PR), Artificial Intelligence Review (AIR), Knowledge-Based Systems (KBS), Neural Networks, Scientific Reports, Journal of Big Data, Cognitive Computation

💻 Internships

1. Research Intern
   Company/Institution: DCML Group, Peking University
   Advisor: Prof. Wentao Zhang
   Employment period: From 01/2024 to the present

2. Research Intern
   Company/Institution: Medical Big Data Research Center, Chinese PLA General Hospital
   Advisor: Dr. Yawei Zhao
   Employment period: From 11/2023 to the present

3. Research Intern
   Company/Institution: AI Research Center, Yunding Technology Co.,Ltd
   Advisor: Researcher Zhen Gao
   Employment period: From 12/2022 to 02/2023

🏆 Honors and Awards

1. Outstanding Master’s Dissertation Award, Shandong (32 people in SDUST, and 548 people in Shandong), 2025
2. Outstanding Master’s Dissertation Award, ShanDong Association of Artificial Intelligence (6 people in Shandong), 2024
3. Outstanding Master’s Dissertation Award, Shandong Computer Federation (27 people in Shandong), 2024