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I am an Assistant Professor of Computer Science at Tsinghua University, where I am a member of the Knowledge Engineering Group (KEG).
I received my Ph.D. in Computer Science from University of Notre Dame.
Before joining Tsinghua, I was a researcher at Meta AI and Microsoft Research Redmond.
My research focuses on data mining, graph representation learning, pre-training models, and social & information networks.
Together with collaborators, recent research includes
the Heterogeneous Graph Transformer (HGT),
graph pre-training (GraphMAE,
and network embedding (NetMF,
some of which were deployed for billion-scale applications in AMiner, Facebook, and Microsoft and nominated for best papers in WWW'22, WWW'19, and WSDM’15.
I was selected as one of the IJCAI’22 Early Career Spotlights
the 2017 ACM SIGKDD Doctoral Dissertation Award Honorable Mention
and 2022 ACM SIGKDD Rising Star Award.
More information about my experience and research can be found on
I am looking for self-motivated students to work with me on graph representation learning, graph neural networks, pre-training techniques, and social networks.
2022: Invited Talk/Tutorial on Graph Representation Learning and Pre-Training. Thanks all collaborators!
2022: KEG releases GLM-130B---an open bilingual pre-trained model with 130 billion parameters.
and the team!
github & model download
2022: IJCAI Early Career Spotlight Invited Talk on Graph Representation Learning and Pre-Training. Thanks all collaborators!
2022: NeurIPS Competition OGB-LGC 2022: A Large-Scale Challenge for Machine Learning on Graphs.
products & best & top*
graph representation learning :
graph neural nets |
heterogeneous & knowledge graphs |
pre-training & self-supervised |
network embedding |
data & benchmarks
social & information networks :
user modeling & profiling |
link prediction |
science of science
Are We Really Making Much Progress? Revisiting, Benchmarking and Refining the Heterogeneous Graph Neural Networks
Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang.
KDD'21 (Proc. of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2021. Full Research Paper.
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs
Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang
KDD'19 (Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2019. Full Applied Data Science Paper (Oral), 6.4%.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec.
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang.
WSDM'18 (Proc. of the 11th ACM International Conference on Web Search and Data Mining), 2018. Full paper (Oral), 16%.
Microsoft Research Blog
2nd most cited paper in WSDM'18
I'm very lucky to have the opportunities to work with these brilliant students and interns (ordered by year & last name).
- 2020, Scott Freitas, 3rd year Ph.D. student at Georgia Tech, summer intern@MSR (co-mentor)
- 2020, Ziniu Hu, 2nd year Ph.D. student at UCLA, summer intern@MSR
- 2020, Namyong Park, 3rd year Ph.D. student at CMU, summer intern@MSR
- 2020, Yu Zhang, 3rd year Ph.D. student at UIUC, summer intern@MSR (co-mentor)
- 2019, Ziniu Hu, 1st year Ph.D. student at UCLA, summer intern@MSR
- 2019, Jialin Zhao, 1st year Master student at UW&Tsinghua GIX, visiting student@MSR
- 2018, Yian Yin, 2nd year Ph.D. student at Northwestern, visiting student@MSR
- 2017, Jiezhong Qiu, 1st year Ph.D. student at Tsinghua, summer intern@MSR (with Dr. Hao Ma)
- 2020, Guest Lecture, Knowledge Graph, Computer Science Department, Stanford University
- 2020, Guest Lecture, Advanced Machine Learning, Computer Science Department, Tsinghua University
- 2020, Tutorial (Invited), Graph Representation Learning, ECML/PKDD'20 slides
- 2019, Tutorial, Representation Learning on Networks (full day), WWW'19 slides
- 2019, Tutorial, Learning from Networks (full day), KDD'19 slides
- 2018, Tutorial, Computational Models for Social and Information Network Analysis, KDD'18 slides
- 2022: Invited Talk at IJCAI Early Career Spotlight
- 2021: Invited Talk at Tsinghua University, Computer Science Department
- 2020: Invited Talk at Beijing AI Conference Knowledge & Intelligence Forum slides-b
- 2020: Invited Talk at CCF Young Elite Forum slides-b
- 2019: Invited Talk at Tsinghua University, Computer Science Department
- 2019: Invited Talk at INFORMS'19 Informs Annual Meeting slides-a
- 2019: Microsoft Security and Compliance AI Summit
- 2019: AI and Tensor Conference at Los Alomos National Lab slides-a
- 2019: Invited Talk at NetSci'19 Satellite on Quantifying Success
- 2019: Invited Talk at NetSci'19 Satellite on Network Representation Learningslides-a
- 2018: Invited Talk at NetSci'18 Higher-Order Models in Network Science Satellite (HONS'18)
- 2018: Invited Talk at NICO, Northwestern University, IL
- 2017: Invited Talk at Labs in Tsinghua University
- 2016: Keynote at ACM JCDL'16 Workshop on Mining Scientific Publications (WOSP'16)
- 2016: Invited Talks at Labs in Stanford University, Tsinghua University, & Chinese Academy of Sciences
- 2015: Invited Talks at Labs in Oxford University, & Hesburgh Library at University of Notre Dame
- Track Co-Chair of WWW'23 Social Network Analysis and Graph Algorithms Track
- Program Co-Chair of ECML/PKDD'21 Applied Data Science Track
- Program Co-Chair of ACM/IEEE ASONAM'21 Industry Track
- Program Co-Chair of ECML/PKDD'20 Applied Data Science Track
- Program Co-Chair of National Conference on Social Media Processing (SMP'20)
- Workshop Co-Chair of SIAM SDM'20
- Deep Learning Day Co-Chair of ACM KDD'20
- Deep Learning Day Co-Chair of ACM KDD'19
- Deep Learning Day Co-Chair of ACM KDD'18
- Associate Editor of IEEE Transactions on Big Data (TBD), 2020--
- Associate Editor of Springer Social Network Analysis and Mining (SNAM), 2021--
- Associate Editor of AI OPEN, 2020--
- Guest Editor of Special Issue "AI for COVID-19" at IEEE TBD, 2020
Conference PC members:
- 2023: WWW (Track Co-Chair), AAAI (Senior PC)
- 2022: KDD (Senior PC), WWW (Senior PC), AAAI (Senior PC), ECML/PKDD (Area Chair)
- 2021: KDD, NeurIPS, AAAI (Senior PC), ECML/PKDD (PC Co-Chair ADS), ASONAM (Industry Track Co-Chair)
- 2020: KDD, NeurIPS, WSDM, WWW, SDM, ECML/PKDD (PC Co-Chair ADS), SMP (PC Co-Chair)
- 2019: KDD, WSDM, WWW, SDM, ICDM, CIKM, AAAI
- 2018: KDD, WSDM, WWW, SDM, ICDM, ECML/PKDD, DSAA
- 2017: KDD, WSDM, WWW, SDM, ASONAM, CIKM
- 2016: ASONAM, CIKM
- 2015: ASONAM
- NeurIPS Competition OGB-LSC 2022: A Large-Scale Challenge for ML on Graphs at NeurIPS'22
- KDD CUP OGB-LGC: A Large-Scale Challenge for Machine Learning on Graphs at KDD'21
- Nature Machine Intelligence
- Nature Human Behavior
- Nature Scientific Reports
- JMLR, Journal of Machine Learning Research
- CSUR, ACM Computing Surveys
- TKDD, ACM Transactions on the Knowledge Discovery from Data
- TWEB, ACM Transactions on the Web
- TKDE, IEEE Transactions on Knowledge and Data Engineering
- TMC, IEEE Transactions on Mobile Computing
- TBD, IEEE Transactions on Big Data
1-309, FIT Building
Beijing 100084, China
*All stats are observed from Google Scholar on Jun. 2022