World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
12921
World Ranking
9505
National Ranking
1204

Overview

Yuxiao Dong is affiliated with Tsinghua University in China and has contributed extensively to the field of computer science. Their research spans primarily within the subfields of artificial intelligence, computer vision and pattern recognition, and information systems, with additional work related to molecular biology and management science and operations research.

Their work covers a range of topics including:

  • Topic Modeling
  • Advanced Graph Neural Networks
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Data Quality and Management
  • Semantic Web and Ontologies
  • Domain Adaptation and Few-Shot Learning

Yuxiao Dong has published numerous papers, with some notable recent publications as follows:

  • Open Graph Benchmark: Datasets for Machine Learning on Graphs, 2020, arXiv (Cornell University)
  • Microsoft Academic Graph: When experts are not enough, 2020, Quantitative Science Studies
  • GraphMAE: Self-Supervised Masked Graph Autoencoders, 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • GLM-130B: An Open Bilingual Pre-trained Model, 2022, arXiv (Cornell University)
  • ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools, 2024, arXiv (Cornell University)

Their frequent coauthors include Jie Tang, Aohan Zeng, Zhengxiao Du, and Yukuo Cen, reflecting collaborations that are repeated across multiple projects.

Yuxiao Dong's work appears predominantly in publication venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Knowledge and Data Engineering
  • IEEE Transactions on Systems Man and Cybernetics Systems
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Proceedings of the ACM Web Conference 2022

In addition to articles, Yuxiao Dong has contributed to book publications with Springer Science+Business Media, authoring volumes related to machine learning and knowledge discovery in databases, including:

  • Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2021
  • Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

Their research outputs contribute to advancing knowledge in machine learning methodologies and applications across diverse fields, with a significant emphasis on graph-based models, language models, and data quality issues.

Best Publications

  • metapath2vec: Scalable Representation Learning for Heterogeneous Networks

    Yuxiao Dong;Nitesh V. Chawla;Ananthram Swami

  • GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

    Jiezhong Qiu;Qibin Chen;Yuxiao Dong;Jing Zhang

  • Heterogeneous Graph Transformer

    Ziniu Hu;Yuxiao Dong;Kuansan Wang;Yizhou Sun

  • Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

    Jiezhong Qiu;Yuxiao Dong;Hao Ma;Jian Li

  • GLM-130B: An Open Bilingual Pre-trained Model

    Unknown

  • DeepInf: Social Influence Prediction with Deep Learning

    Jiezhong Qiu;Jian Tang;Hao Ma;Yuxiao Dong

  • GPT-GNN: Generative Pre-Training of Graph Neural Networks

    Ziniu Hu;Yuxiao Dong;Kuansan Wang;Kai-Wei Chang

  • Microsoft Academic Graph: When experts are not enough

    Kuansan Wang;Zhihong Shen;Chiyuan Huang;Chieh-Han Wu

  • GraphMAE: Self-Supervised Masked Graph Autoencoders

    Unknown

  • Inferring social status and rich club effects in enterprise communication networks.

    Yuxiao Dong;Jie Tang;Nitesh V. Chawla;Tiancheng Lou

  • Link Prediction and Recommendation across Heterogeneous Social Networks

    Yuxiao Dong;Jie Tang;Sen Wu;Jilei Tian

  • Inferring user demographics and social strategies in mobile social networks

    Yuxiao Dong;Yang Yang;Jie Tang;Nitesh V. Chawla

  • Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks

    Qingsong Lv;Ming Ding;Qiang Liu;Yuxiang Chen

  • NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

    Jiezhong Qiu;Yuxiao Dong;Hao Ma;Jian Li

  • CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X

    Unknown

  • ProNE: Fast and Scalable Network Representation Learning

    Jie Zhang;Yuxiao Dong;Yan Wang;Jie Tang

  • MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems

    Tinglin Huang;Yuxiao Dong;Ming Ding;Zhen Yang

  • A link clustering based overlapping community detection algorithm

    Chuan Shi;Yanan Cai;Di Fu;Yuxiao Dong

  • A Review of Microsoft Academic Services for Science of Science Studies.

    Kuansan Wang;Zhihong Shen;Chiyuan Huang;Chieh-Han Wu

  • A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

    Yuxiao Dong;Hao Ma;Zhihong Shen;Kuansan Wang

  • Will This Paper Increase Your h-index?: Scientific Impact Prediction

    Yuxiao Dong;Reid A. Johnson;Nitesh V. Chawla

  • CoupledLP: Link Prediction in Coupled Networks

    Yuxiao Dong;Jing Zhang;Jie Tang;Nitesh V. Chawla

  • OAG: Toward Linking Large-scale Heterogeneous Entity Graphs

    Fanjin Zhang;Xiao Liu;Jie Tang;Yuxiao Dong

Frequent Co-Authors

Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
Jie Tang
Jie Tang Tsinghua University
Kuansan Wang
Kuansan Wang Microsoft (United States)
Hao Ma
Hao Ma Facebook (United States)
Ananthram Swami
Ananthram Swami United States Army Research Laboratory
Omar Lizardo
Omar Lizardo University of California, Los Angeles
Yizhou Sun
Yizhou Sun University of California, Los Angeles
Xiaoming Fu
Xiaoming Fu University of Göttingen
Chuan Shi
Chuan Shi Beijing University of Posts and Telecommunications

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