Yuanchen Bei

I am now a final-year master’s student in the College of Computer Science and Technology, Zhejiang University, advised by Prof. Sheng Zhou and Prof. Jiajun Bu. I also work closely with Prof. Hao Chen at Macau CityU, Prof. Feiran Huang at JNU, and Prof. Xiao Huang at PolyU. They are all very supportive and kind.

“There should be (explicit/implicit) relations among real-world data.” My research interests currently include graph machine learning, relation foundation models (especially on graph and multimodal data), and social computing (especially on user behavior analysis and network analysis). In my free time, I am also interested in finance and economics. Please kindly reach out to me if you have any questions or cooperation interests.

👨‍💻‍ I am actively looking for Ph.D. opportunities in Fall 2025, and I sincerely appreciate any opportunity!

News

• 2024.11:  🎉 Our paper on GNNs for Ambiguous Classification is accepted by KDD 2025.
• 2024.10:  🎉 Our paper on LLM-based Cold-Start Recommendation is accepted by WSDM 2025.
• 2024.10:  🎉 Our paper on Correlation-based GNNs for Recommendation” is accepted by TKDE 2024.
• 2024.09:  🎉 Our paper on Out-of-Vocabulary Item Recommendation is accepted by NeurIPS 2024 as a spotlight.
• 2024.09:  📚 Invited as the reviewer for ICLR 2025.
• 2024.07:  📚 Invited as the reviewer for KDD 2025.
• 2024.05:  🎉 Our paper on GNNs for User Multi-Behavior Modeling” is accepted by KDD 2024.
• 2024.01:  🎉 Our paper on Macro GNNs for Billion-Scale Recommendation is accepted by WWW 2024.
• 2023.12:  🎉 Our paper on Dynamic GNN Pre-Training” is accepted by ICDE 2024.
• 2023.09:  🎉 Our paper on Neighborhood Selection for Unsupervised Graph Anomaly Detection is accepted by ICDM 2023.

Publications

Published Papers (* co-first author):

• Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification. (KDD, 2025)

  Yuanchen Bei, Weizhi Chen, Hao Chen, Sheng Zhou, Carl Yang, Jiapei Fan, Longtao Huang, Jiajun Bu.

  

• LLM Behavior Simulator for Online Billion-Scale Item Cold-Start Recommendation. (WSDM, 2025)

  Feiran Huang, Yuanchen Bei, Zhenghang Yang, Junyi Jiang, Hao Chen, Qijie Shen, Senzhang Wang, Fakhri Karray, Philip Yu.

  

• Macro Graph Neural Networks for Online Billion-Scale Recommender Systems. (WWW, 2024)

  Hao Chen*, Yuanchen Bei*, Qijie Shen, Yue Xu, Sheng Zhou, Wenbing Huang, Feiran Huang, Senzhang Wang, and Xiao Huang.

  

• Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks. (KDD, 2024)

  Yijie Zhang*, Yuanchen Bei*, Hao Chen*, Qijie Shen, Zheng Yuan, Huan Gong, Senzhang Wang, Feiran Huang, and Xiao Huang.

  

• CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks. (ICDE, 2024)

  Yuanchen Bei, Hao Xu, Sheng Zhou, Huixuan Chi, Haishuai Wang, Mengdi Zhang, Zhao Li, and Jiajun Bu.

  

• Fine-Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination. (NeurIPS, 2024)

  Ruochen Liu, Hao Chen, Yuanchen Bei, Qijie Shen, Fangwei Zhong, Senzhang Wang, and Jianxin Wang.

  

• Feedback Reciprocal Graph Collaborative Filtering. (CIKM, 2024)

  Weijun Chen*, Yuanchen Bei*, Qijie Shen, Hao Chen, Xiao Huang, and Feiran Huang.

  

• Graph Cross-Correlated Network for Recommendation. (TKDE, 2024)

  Hao Chen*, Yuanchen Bei*, Wenbing Huang, Shengyuan Chen, Feiran Huang, and Xiao Huang.

  

• Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection. (ICDM, 2023)

  Yuanchen Bei, Sheng Zhou, Qiaoyu Tan, Hao Xu, Hao Chen, Zhao Li, and Jiajun Bu.

  

• Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction. (CIKM, 2023)

  Yuanchen Bei, Hao Chen, Shengyuan Chen, Xiao Huang, Sheng Zhou, and Feiran Huang.

  

Preprint Papers:

• Revisiting the Message Passing in Heterophilous Graph Neural Networks.

  Zhuonan Zheng, Yuanchen Bei, Sheng Zhou, Yao Ma, Ming Gu, Hongjia Xu, Chengyu Lai, Jiawei Chen, and Jiajun Bu.

  

• CLR-Bench: Evaluating Large Language Models in College-level Reasoning.

  Junnan Dong, Zijin Hong, Yuanchen Bei, Feiran Huang, Xinrun Wang, Xiao Huang.

  

• Better Late Than Never: Formulating and Benchmarking Recommendation Editing.

  Chengyu Lai, Sheng Zhou, Zhimeng Jiang, Qiaoyu Tan, Yuanchen Bei, Jiawei Chen, Ningyu Zhang, and Jiajun Bu.

  

• Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection.

  Yuanchen Bei, Sheng Zhou, Jinke Shi, Yao Ma, Haishuai Wang, and Jiajun Bu.

  

Workshop Papers:

• Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering. (IWLKG@ICDM, 2023)

  Yijie Zhang, Yuanchen Bei, Shiqi Yang, Hao Chen, Zhiqing Li, Lijia Chen, and Feiran Huang.

  

• Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation. (KDAH@CIKM, 2023)

  Huixuan Chi, Hao Xu, Mengya Liu, Yuanchen Bei, Sheng Zhou, Danyang Liu, and Mengdi Zhang.

  

• Flattened Graph Convolutional Networks For Recommendation. (DLP@KDD, 2022)

  Yue Xu, Hao Chen, Zengde Deng, Yuanchen Bei, and Feiran Huang.

  

Patents:

• Text Sentiment Analysis Method Based on Multi-Level Graph Pooling. (US Patent, No. 11,687,728, 2023)

  Feiran Huang, Zhiquan Liu, and Yuanchen Bei.

• Social Recommendation Method Based on Multi-Feature Heterogeneous Graph Neural Networks. (US Patent, No. 11,631,147, 2023)

  Feiran Huang, Guan Liu, and Yuanchen Bei.

Selected Open-Source Project

• Graph Pre-Training Literature Library.

  

• Cold-Start Recommendation Literature Library.

  

• ColdRec: A Comprehensive Benchmark for Cold-Start Recommendation. Including 20+ cold-start recommendation models with comprehensive evaluations.

  

• DreamerGPT: Instruction-Tuning for Large Language Model with Chinese Corpus.

  

• DGraph-Fin: A Large-Scale Dynamic Graph Anomaly Detection Leaderboard.

  

Honors and Awards

• 2024.11, National Scholarship (Top 1%, Master).
• 2023.11, First Class Scholarship, Zhejiang University.
• 2022.06, Outstanding Undergraduate Student & Thesis.
• 2021.12, National Scholarship (Top 1%, Bachelor).
• 2020.05, First Class Scholarship, Jinan University.

Academic Services

• Conference Reviewer: KDD (2024, 2025), ICLR 2025, ECAI 2024.
• Journal Reviewer: ACM TKDD (Since 2023), IEEE TKDE (Since 2024), IEEE TNNLS (Since 2024), IEEE TII (Since 2024).

Experiences

• 2022.09 - 2025.03, Research Assistant, Eagle Lab@Zhejiang University, Hangzhou, China.
• 2024.07 - 2024.10, Research Assistant, DEEP Lab@The Hong Kong Polytechnic University, Hong Kong SAR, China.
• 2023.06 - 2023.11, Research Intern, CRO Security Technology Team@Alibaba Group, Hangzhou, China.
• 2022.02 - 2022.08, Research Intern, NLP Center@Meituan, Beijing, China.
• 2019.10 - 2022.01, Research Assistant, RecSys Group@Jinan University, Guangzhou, China.