Ziwei Zhang

Ziwei Zhang

Associate professor in the School of Computer Science and Engineering, Beihang University

ZIWEI ZHANG

This is the homepage of ZIWEI ZHANG 张子威, an associate professor in the School of Computer Science and Engineering, Beihang University. Prior to that, I was a postdoc researcher in the Department of Computer Science and Technology, Tsinghua University, working with Prof. Wenwu Zhu, Prof. Peng Cui, and Prof. Xin Wang.

I am looking for self-motivated undergraduate interns and master students with solid mathematical backgrounds and coding skills. Feel free to drop me an email with CV if interested.

所在课题组（负责人：李建欣教授）每年有若干硕士与博士名额，感兴趣请尽量提前联系。

I am also open to discussions and collaborations in graph machine learning related topics. Feel free to drop me an email!

News

  • [May 2024] Two papers about lightweight graph neural architecture search and LLM on dynamic graphs are accepted by KDD 2024!
  • [May 2024] Two papers about graph out-of-distribution generalization and continual graph neural architecture search are accepted by ICML 2024!
  • [Dec 2023] Two papers about out-of-distribution graph neural architecture search are accepted by AAAI 2024!
  • [Oct 2023] We wrote a perspective paper about Large Graph Model, which is accepted by NeurIPS GLFrontiers workshop! See more here and paper collection
  • [Sep 2023] Four papers are accepted by NeurIPS! SILD and EA-DGNN explore spectral and environment persepective of dynamic graph OOD generalization, while MTGC3 and DSGAS study multi-task and unsupervised graph NAS!
  • [Jul 2023] I gave a talk about Automated Graph Machine Learning at DataFun.
  • [Nov 2022] I gave two talks about out-of-distribution (OOD) generalization on graphs and Automated Machine Learning on Graphs at Learning on Graphs (LOGS).
  • [Nov 2020] We have opensourced AutoGL, a toolkit and platform towards automatic machine learning on graphs. Feel free to have a try and give us feedbacks!

Biography

Ziwei Zhang is currently an associate professor at Beihang University. He received his B.S. from Mathematics and Physics and Ph.D. from the Department of Computer Science and Technology, Tsinghua University, in 2016 and 2021, respectively. His current research interests focus on graph machine learning, including graph neural network (GNN), graph out-of-distribution generalization, and automated graph learning. He is also interested in exploring graph foundation models and applying graph machine learning into AI4Science, particularly bioinformatics. He has published over 40 papers in prestigious conferences and journals, including ICML, NeurIPS, KDD, ICLR, CVPR, AAAI, IJCAI, and TKDE.

Publications

2024

  • [C32] New! Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Wenwu Zhu. LLM4DyG: Can LLMs Solve Spatial-Temporal Problems on Dynamic Graphs? KDD, 2024. (Full Paper, CCF-A) (Paper) (arXiv)
  • [C31] New! Beini Xie, Heng Chang, Ziwei Zhang, Zeyang Zhang, Simin Wu, Xin Wang, Yuan Meng, Wenwu Zhu. Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification. KDD, 2024. (Full Paper, CCF-A) (Paper)
  • [C30] New! Haoyang Li, Xin Wang, Zeyang Zhang, Haibo Chen, Ziwei Zhang, Wenwu Zhu. DDisentangled Graph Self-supervised Learning for Out-of-Distribution Generalization. ICML, 2024. (Full Paper, CCF-A) (Paper)
  • [C29] New! Zeyang Zhang, Xin Wang, Yijian Qin, Hong Chen, Ziwei Zhang, Xu Chu, Wenwu Zhu. Disentangled Continual Graph Neural Architecture Search with Invariant Modularization. ICML, 2024. (Full Paper, CCF-A) (Paper)
  • [C28] Yang Yao, Xin Wang, Yijian Qin, Ziwei Zhang, Wenwu Zhu, Hong Mei. Customized Cross-device Neural Architecture Search with Images. ICME, 2024. (Full Paper, CCF-B)
  • [C27] New! Jie Cai, Xin Wang, Haoyang Li, Ziwei Zhang, Wenwu Zhu. Multimodal Graph Neural Architecture Search Under Distribution Shifts. AAAI, 2024. (Full Paper, CCF-A) (Paper)
  • [C26] New! Yang Yao, Xin Wang, Yijian Qin, Ziwei Zhang, Wenwu Zhu, Hong Mei. Data-augmented Curriculum Graph Neural Architecture Search Under Distribution Shifts. AAAI, 2024. (Full Paper, CCF-A) (Paper)

2023

  • [W2] New! Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, Wenwu Zhu. Graph Meets LLMs: Towards Large Graph Models. NeurIPS GLFrontiers workshop, 2023 (Paper) (Paper Collection)
  • [C25] New! Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu. Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts. NeurIPS, 2023. (CCF-A) (Paper) (Code)
  • [C24] New! Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao Shen, Shiqi Shen, Wenwu Zhu. Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision. NeurIPS, 2023. (CCF-A) (Paper) (Code)
  • [C23] New! Yijian Qin, Xin Wang, Ziwei Zhang, Hong Chen, Wenwu Zhu. Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum. NeurIPS, 2023. (CCF-A) (Paper) (Code)
  • [C22] New! Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, Jianxin Li. Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization. NeurIPS, 2023. (CCF-A) (Paper) (Code)
  • [C21] Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang, Daixin Wang, Zhiqiang Zhang, Zhitao Ying, Wenwu Zhu. Adversarially Robust Neural Architecture Search for Graph Neural Networks. CVPR, 2023. (CCF-A) (Paper)
  • [C20] Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu. Intention-aware Sequential Recommendation with Structured Intent Transition. ICDE, 2023. (Extended Abstract, CCF-A)
  • [C19] Zizhao Zhang, Xin Wang, Chaoyu Guan, Ziwei Zhang, Haoyang Li, Wenwu Zhu. AutoGT: Automated Graph Transformer Architecture Search. ICLR, 2023. (Oral ) (Paper) (Code)
  • [C18] Zeyang Zhang, Ziwei Zhang, Xin Wang, Yijian Qin, Zhou Qin, Wenwu Zhu. Dynamic Heterogeneous Graph Attention Neural Architecture Search. AAAI, 2023. (Full Paper, CCF-A) (Paper) (Code)
  • [J13] Yang Liao, Jing Zhao, Jiyong Bian, Ziwei Zhang, Siqi Xu, Yijian Qin, Shiyu Miao, Rui Li, Ruiping Liu, Meng Zhang, Wenwu Zhu, Huijuan Liu, Jiuhui Qu. From mechanism to application: decrypting light-regulated denitrifying microbiome through geometric deep learning. iMeta, 2023. (IF 23.7, JCR-Q1)
  • [J12] Fang Shen, Jialong Wang, Ziwei Zhang, Xin Wang, Yue Li, Zhaowei Geng, Bing Pan, Zengyi Lu, Wendy Zhao, Wenwu Zhu. Long-term Multivariate Time Series Forecasting in Data Centers Based on Multi-factor Separation Evolutionary Spatial-Temporal Graph Neural Networks. KBS, 2023.
  • [J11] 张子威，王鑫，朱文武。图神经架构搜索综述. 计算机学报，2023. (综述文章, CCF-A) (Paper)
  • [J10] Haoyang Li, Ziwei Zhang, Xin Wang, Wenwu Zhu. Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments. TOIS, 2023. (Full Paper, CCF-A) (Paper)

2022

  • [C17] Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu. NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search. NeurIPS, 2022. (Full Paper, CCF-A) (Paper) (Code)
  • [C16] Haoyang Li, Ziwei Zhang, Xin Wang, Wenwu Zhu. Learning Invariant Graph Representations Under Distribution Shifts. NeurIPS, 2022. (Full Paper, CCF-A) (Paper)
  • [C15] Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Zhou Qin, Wenwu Zhu. Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift. NeurIPS, 2022. (Full Paper, CCF-A) (Paper) (Code)
  • [C14] Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, Wenwu Zhu. Large-Scale Graph Neural Architecture Search. ICML, 2022. (Full Paper, CCF-A) (Paper) (Code) (Code)
  • [C13] Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, Wenwu Zhu. Graph Neural Architecture Search Under Distribution Shifts. ICML, 2022. (Full Paper, CCF-A) (Paper) (Code)
  • [C12] Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu. Parametric Visual Program Induction with Function Modularization. ICML, 2022. (Full Paper, CCF-A) (Paper)
  • [C11] Zeyang Zhang, Ziwei Zhang, Xin Wang, Wenwu Zhu. Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum. AAAI, 2022. (Full Paper, CCF-A) (Paper) (Code)
  • [J9] Haoyang Li, Ziwei Zhang, Xin Wang, Wenwu Zhu. Disentangled Graph Contrastive Learning with Independence Promotion. TKDE, 2022. (Full Paper, CCF-A) (paper)
  • [J8] Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu. OOD-GNN: Out-of-Distribution Generalized Graph Neural Network. TKDE, 2022. (Full Paper, CCF-A) (Paper)
  • [J7] Ziwei Zhang, Chenhao Niu, Peng Cui, Jian Pei, Bo Zhang, Wenwu Zhu. Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing. TKDE, 2022. (Full Paper, CCF-A) (paper)
  • [J6] Xumin Chen, Ruobing Xie, Zhejie Qiu, Peng Cui, Ziwei Zhang, Shukai Liu, Shiqiang Yang, Bo Zhang, Leyu Lin. Group-based Social Diffusion in Recommendation. World Wide Web Journal, 2022. (CCF-B)

2021

  • [C10] Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu. Disentangled Contrastive Learning on Graphs. NeurIPS, 2021. (Full Paper, CCF-A) (Paper) (Code) (Slides)
  • [C9] Haoxin Liu*, Ziwei Zhang*, Peng Cui, Yafeng Zhang, Qiang Cai, Jiashuo Liu, Wenwu Zhu. Signed Graph Neural Network with Latent Groups. KDD, 2021. (Full Paper, acceptance rate 20.5%, *: equal contribution, CCF-A) (Paper) (Code) (Slides)
  • [C8] Ziwei Zhang, Xin Wang, Wenwu Zhu. Automated Machine Learning on Graphs: A Survey. IJCAI 2021. (Survey track, CCF-A) (Paper) (Paper Collection) (Slides)
  • [C7] Fang Shen, Zhan Li, Bing Pan, Ziwei Zhang, Jialong Wang, Wendy Zhao, Xin Wang, Wenwu Zhu. Inter-and-Intra Domain Attention Relational Inference for Rack Temperature Prediction in Data Center. DASFAA, 2022. (CCF-B)
  • [J5] Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, Wenwu Zhu. Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs. TKDE, 2021. (Full Paper, CCF-A) (Paper) (Code)
  • [J4] Ruobing Xie, Qi Liu, Shukai Liu, Ziwei Zhang, Peng Cui, Bo Zhang, Leyu Lin. Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network. IEEE Transactions on Big Data, 2021.(Paper)
  • [J3] Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu. Intention-aware Sequential Recommendation with Structured Intent Transition. TKDE, 2021.(Full Paper, CCF-A) (Paper) (Slides)
  • [W1] Chaoyu Guan*, Ziwei Zhang*, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu. AutoGL: A Library for Automated Graph Learning. ICLR 2021 GTRL Workshop. (*: equal contribution) (Paper) (Link)

Before 2020

  • [C6] Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu. Robust Graph Convolutional Networks against Adversarial Attacks. KDD, 2019. (Full Paper, Oral, acceptance rate 9.1%, CCF-A) (Page) (Paper) (Code)
  • [C5] Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu. Billion-scale Network Embedding with Iterative Random Projection. ICDM, 2018. (Full Paper, Oral, acceptance rate 8.9%, CCF-B) (Page) (Paper) (Code) (Slides)
  • [C4] Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Xuanrong Yao, Wenwu Zhu. Arbitrary-Order Proximity Preserved Network Embedding. KDD, 2018. (Full Paper, Oral, acceptance rate 10.8%, CCF-A) (Page) (Paper) (Code) (Slides)
  • [C3] Xiao Wang*, Ziwei Zhang*, Jing Wang, Peng Cui, Shiqiang Yang. Power-law Distribution Aware Trust Prediction. IJCAI, 2018. (Full Paper, Oral, acceptance rate 20.5%,*: equal contribution, CCF-A) (Page) (Paper) (Code)
  • [C2] Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu. TIMERS: Error-Bounded SVD Restart on Dynamic Networks. AAAI, 2018. (Full Paper, Oral, acceptance rate 11%, CCF-A) (Page) (Paper) (Code) (Slides)
  • [C1] Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu. Asymmetric Transitivity Preserving Graph Embedding. KDD, 2016. (Full Paper, Oral, acceptance rate 8.9%, CCF-A) (Page) (Paper) (Code) (Slides)
  • [J2] Ziwei Zhang, Peng Cui, Wenwu Zhu. Deep Learning on Graphs: A Survey. TKDE, 2020. (Survey Paper, CCF-A) (Paper)
  • [J1] Dingyuan Zhu, Peng Cui, Ziwei Zhang, Jian Pei, Wenwu Zhu. High-order Proximity Preserved Embedding for Dynamic Networks. TKDE, 2018. (CCF-A) (Page) (Paper) (Code)

Preprints

  • Peiwen Li, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Jialong Wang, Yang Li, Wenwu Zhu. Causal-Aware Graph Neural Architecture Search under Distribution Shifts. arXiv 2405.16489 (Paper)
  • Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Xu Chu, Yuekui Yang, Wenwu Zhu, Hong Mei. Exploring the Potential of Large Language Models in Graph Generation. arXiv 2403.14358 (Paper)
  • Jun Zhu, Zeyang Zhang, Yujia Xiang, Beini Xie, Xinwen Dong, Linhai Xie, Peijie Zhou, Rongyan Yao, Xiaowen Wang, Yang Li, Fuchu He, Wenwu Zhu, Ziwei Zhang#, Cheng Chang. Decoding cell identity with multi-scale explainable deep learning. bioarXiv 2024.02.05.578922 (#: co-corresponding authors) (paper)
  • Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu. Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion. arXiv 2311.14255 (Paper)
  • Yijian Qin, Xin Wang, Ziwei Zhang, Wenwu Zhu. Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs. arXiv 2310.18152 (Paper)
  • Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu. Out-Of-Distribution Generalization on Graphs: A Survey. arXiv:2202.07987. (Paper) (Paper Collection)
  • Xin Wang*, Ziwei Zhang*, Wenwu Zhu. Automated Graph Machine Learning: Approaches, Libraries and Directions. arXiv:2201.01288. (*: equal contribution) (Paper)
  • Ziwei Zhang, Xin Wang, Zeyang Zhang, Peng Cui, Wenwu Zhu. Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need? arXiv:2112.12345 (Paper)

Talks

  • [Nov 2023] Invited talk at DSA Thrust Seminar, HKUST(Guangzhou)
  • [Jul 2023] Invited talk at DataFun Slides
  • [Nov 2022] Invited talk at Learning on Graphs
  • [May 2022] Invited talk at Noah’s Ark Lab, Huawei Technologies
  • [Apr 2022] Invited talk at Center for Data Science, Peking University
  • [Mar 2022] Invited talk at University of Chinese Academy of Sciences
  • [Aug 2021] Tutorial at SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021 Slides
  • [Jul 2021] Invited talk at Cognitive Computing Lab (CCL), Baidu Research
  • [Oct 2020] Tutorial at Natural Language Processing and Chinese Computing (NLPCC) 2020 Slides
  • [Dec 2019] Guest Lecture at Introduction to Artificial Intelligence class of Nankai University
  • [Mar 2019] Invited talk at CoLab, Beihang University
  • [Jun 2018] Invited talk at SigNAL, Institute of Computing Technology(ICT), Chinese Academy of Sciences

Professional Activities

VALSE (Vision And Learning SEminar) EACC (Executive Area Chairs Committee), CCF Expert Committee on Big Data, CCF AI Graph Task Group member

Conference Reviewer: NeurIPS 2020-2024, ICLR 2021-2025, KDD 2021-2025, ICML 2021-2024, AAAI 2021-2025, IJCAI 2021-2024, WWW 2022-2025, WSDM 2023-2025, LoG 2022-2024, CIKM 2019

Journal Reviewer: Nature Computational Science, IEEE Trans. on Knowledge and Data Engineering (TKDE), Journal of Machine Learning Research (JMLR), IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Trans. on Neural Networks and Learning Systems (TNNLS), Neural Processing Letters (NEPL), etc.

Awards

  • 2024 Top 2% Scientists by Stanford/Elsevier
  • 2023 The second prize of the State Natural Science Award, P.R.CHINA (国家自然科学二等奖)
  • 2022 The first prize award of natural science research of Ministry of Education (教育部自然科学一等奖)
  • 2022 AI 2000 Most Influential Scholar (2022,2023,2024)
  • 2022 Outstanding Doctoral Dissertation Honorable Mention of CAAI (吴文俊人工智能优秀博士学位论文提名)
  • 2022 China National Postdoctoral Program for Innovative Talents (博士后创新人才支持计划)
  • 2021 Outstanding Doctoral Graduate in Tsinghua University (top 3 in the department)
  • 2021 Outstanding Doctoral Dissertation in Tsinghua University (top 7 in the department)
  • 2021 Baidu’s Global Top 100 Chinese Rising Stars in AI

Media Coverage

  • Dec 2020, AutoGL toolkit is reported by Jiqizhixin and The Paper with more than 23 thousand reads in total
  • Dec 2018, Deep Learning on Graphs Survey is reported by multiple WeChat Official Accounts with more than 22 thousand reads in total

Supervision

I am fortunate to have collaborated with the following students in Tsinghua University (incomplete):

  • Z. Zhang (2025, Ph.D.)
  • Y. Qin (2024, Ph.D. $\rightarrow$ Top Minds researcher at Huawei)
  • H. Li (2023, Ph.D. $\rightarrow$ Postdoc at Weill Cornell Medicine)
  • J. Cai (2023, master $\rightarrow$ Ph.D. at USC)
  • B. Xie (2023, master)
  • C. Guan (2022, master $\rightarrow$ founder of Qingmao Intelligence)
  • H. Liu (2021, master $\rightarrow$ Ph.D. at Gatech)
  • C. Niu (2020, undergraduate $\rightarrow$ master at CMU)