About Me

Wendong is a master student at Prof. Huawei Shen’s Group at the Institue of Computing Technology (ICT), Chinese Academy of Sciences, and he will graduate in June 2023. Wendong’s research interests mainly focus on the Machine Learning and Graph Representation Learning, especially the applied research of Graph Representation Learning, Graph Neural Networks.

Prior to joining ICT, he recieved the bachelor degree in computer science and technology (machine learning) from ShangHaiJiaoTong University (SJTU) in 2020. And he used to do research in ThinkLab (Prof. Junchi Yan’s Group) at MoE-AI-Lab@SJTU from 2019 to 2020.

Research Interests

Wendong’s primary research interest covers the following areas:

• Graph Representation Learning & Graph Neural Networks;
• Machine Learning & Data Mining;
• Social Networks, Financial Networks;
• Recommender System;

And Wendong’s recent research interests also include AIGC (e.g., GPT-X, Language/Graph/Visual Generated Models).

Welcome for cooperation!

• Email: biwendong20g@ict.ac.cn

• Wechat: AydenBi

Career

• Tencent 2023.07~

  Applied Researcher@IEG

  Joined by Tencent Technology Master talent plan

• UbiQuant 2022.10~2022.12

  Quant Researcher

  • Research on Graph Alternative Factor Mining
    • Mining quant factor from stock graph data (e.g., supply chain, industry chain) with graph representation learning methods.

• Microsoft Research Asia (MSRA) 2021.10~2022.10

  Research Internship@DKI-Group, Beijing, Stars of Tomorrow Internship

  • Research on Graph Representation Learning, Graph Neural Networks.
    • GNN, Data mining, Heterophily Graph, Heterogeneous Graph.
    • Papers and international algorithm competitions.

• Byte Dance 2020.10~2021~05

  Algorithm Engineer Internship@AI-Lab-VC, Beijing

  • Participated in the Douyin Natural Project, responsible for the training and deployment of large-scale fine-grained image classification models.

• JD.com 2019.10~2020~06

  Algorithm Engineer Internship@JD AI Research, Shanghai

  • Product image intelligent generation based on product attributes and UV conversion rate.

Publications

• Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network [pdf]

  W. Bi, B. Xu, X. Sun, L. Xu, H. Shen, X. Cheng

  In The ACM Web Conference 2023 (WWW’2023)

• Homophily-oriented Heterogeneous Graph Rewiring [pdf]

  J. Guo, L. Du, W. Bi, Q. Fu, X. Ma, X. Chen, S. Han, D. Zhang, Y. Zhang

  In The ACM Web Conference 2023 (WWW’2023)

• Company-as-Tribe: Company Financial Risk Assessment on Tribe-style Graph with Hierarchical Graph Neural Network [pdf] [video]

  W. Bi, B. Xu, X. Sun, Z. Wang, H. Shen, X. Cheng

  In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’22)

• MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution [pdf] [video]

  W. Bi, L. Du, Q. Fu, Y. Wang, S. Han, D. Zhang

  In The International Conference on Web Search and Data Mining (WSDM’23)

• HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction [pdf]

  C. Yue, L. Du, Q. Fu, W. Bi, H. Liu, Y. Gu, D. Yao

  In The International Conference on Web Search and Data Mining (WSDM’22)

  2nd prize in ACM WSDM Cup 2022

• Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach [pdf]

  W. Bi, L. Du, Q. Fu, Y. Wang, S. Han, D. Zhang

  In arXiv, paper under review

• Towards Better Understanding of Disentangled Representations via Mutual Information [pdf]

  X. Yang, W. Bi, Y. Sun, Y. Cheng, J. Yan

  In arXiv, paper under review