Bingbing Xu

(徐冰冰)

Introduction

I am currently an associate professor at the CAS Key Laboratory of AI Safety. I obtained my PhD degree from Institute of Computing Technology, Chinese Academy of Sciences in 2021, under the supervision of Prof. Xueqi Cheng and Prof. Huawei Shen .

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We are looking for students (third/fourth year students in university or masters) as interns to collaborate with us in research, as well as assistant professors and postdoc related to graph deep learning.

If you are interested, please feel free to contact us at any time.

Publications

1. Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
   
   Zixu Wang, Bingbing Xu, Yige Yuan, Huawei Shen, Xueqi Cheng
   

   C SIGIR 2024
2. TEA: Test-time Energy Adaptation
   
   Yige Yuan, Bingbing Xu, Liang Hou, Fei Sun, Huawei Shen, Xueqi Cheng
   

   C IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR 2024)

   [Paper] [Code] [Summary]

   

   TL;DR: In this paper, we propose to investigate generalization from an energy-based perspective and introduce TEA, a test-time adaptation method which transforms the trained classifier into an energy-based model and aligns the model's distribution with the test data's, enhancing its ability to perceive test distributions and thus improving overall generalizability.

3. PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion
   
   Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou, Fei Sun, Huawei Shen, Xueqi Cheng
   

   C Annual AAAI Conference on Artificial Intelligence (AAAI 2024)

   [Paper] [Code] [Poster] [Sildes] [Video] [Summary]

   

   TL;DR: In this paper, we propose to investigate generalization from PDE perspective and propose PDE-ADD framework. We introduce adaptive distributional diffusion into transport equation to enhance smoothness of its solution, thereby improving generalization directly via the underlying function of NN.

4. Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
   
   Yige Yuan, Bingbing Xu, Huawei Shen, Qi Cao, Keting Cen, Wen Zheng, Xueqi Cheng
   

   J Neural Networks (NN), Volume 172, April 2024

   [Paper] [Code] [Summary]

   

   TL;DR: In this paper, we propose a GCL generalization ability metric and prove a MI upper bound for it from an information-theoretic perspective. Guided by the bound, we design an InfoAdv framework, which can be applied to current GCL models and achieves SOTA performance.

5. History Driven Sampling for Scalable Graph Neural Networks
   
   Yang Li, Bingbing Xu, Fei Sun, Qi Cao, Yige Yuan, and Huawei Shen
   

   C (DASFAA 2024)
6. Graph Domain Adaptation: Challenges, Progress and Prospects
   
   Boshen Shi, Yongqing Wang, Fangda Guo, Bingbing Xu, Huawei Shen, Xueqi Cheng
   
7. Bridged-gnn: Knowledge bridge learning for effective knowledge transfer
   
   Wendong Bi, Xueqi Cheng, Bingbing Xu, Xiaoqian Sun, Li Xi, Huawei Shen
   

   C CIKM 2023

   [Code]
8. DCGNN: Dual-Channel Graph Neural Network for Social Bot Detection
   
   Nuoyan Lyv, Bingbing Xu, Fangda Guo, and Huawei Shen
   

   C CIKM 2023
9. DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph
   
   Kaike Zhang, Qi Cao, Gaolin Fang, Bingbing Xu, Hongjian Zou, Huawei Shen, and Xueqi Cheng
   

   C KDD 2023
10. MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification
    
    Wen Zheng, Bingbing Xu, Emiao Lu, Yang Li, Xuan Zong, and Huawei Shen
    

    C KDD 2023
11. Simple Multi-view Can Bring Powerful Graph Neural Network
    
    Bingbing Xu, Yang Li Qi Cao, and Huawei Shen
    

    C Companion proceedings of WWW 2023
12. Towards deeper graph neural networks via layer-adaptive
    
    Bingbing Xu, Bin Xie, and Huawei Shen
    

    C Companion proceedings of WWW 2023
13. Predicting the silent majority on graphs: Knowledge transferable graph neural network
    
    Wendong Bi, Bingbing Xu, Xiaoqian Sun, Li Xu, Huawei Shen, Xueqi Cheng
    

    C WWW 2023

    [Code]
14. When spatio-temporal meet wavelets: Disentangled traffic forecasting via efficient spectral graph attention networks
    
    Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, and Liang Zeng
    

    C ICDE 2023
15. Location-aware convolutional neural networks for graph classification
    
    Zhaohui Wang, Qi Cao, Huawei Shen, Bingbing Xu, Keting Cen and Xueqi Cheng
    

    J Neural Networks (NN)
16. Company-as-tribe: Company financial risk assessment on tribe-style graph with hierarchical graph neural networks
    
    Wendong Bi, Bingbing Xu, Xiaoqian Sun, Li Xu, Huawei Shen, Xueqi Cheng
    

    C KDD 2022

    [Code]
17. Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional random field
    
    Bingbing Xu, Huawei Shen, Bingjie Sun, Rong An, Qi Cao, and Xueqi Cheng
    

    C AAAI 2021
18. Label-consistency based graph neural networks for semi-supervised node classification
    
    Bingbing Xu, Junjie Huang, Liang Hou, Huawei Shen, Jinhua Gao and Xueqi Cheng
    

    C SIGIR 2020

    [Code]
19. Graph convolutional networks using heat kernel for semi-supervised learning
    
    Bingbing Xu, Huawei Shen, Qi Cao, Keting Cen and Xueqi Cheng
    

    C IJCAI 2019

    [Code]
20. Graph wavelet neural network
    
    Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu and Xueqi Cheng
    

    C ICLR 2019

    [Code]