World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
9239
World Ranking
4648
National Ranking
622

Overview

James Cheng is affiliated with the Chinese University of Hong Kong in China. Their research focuses primarily on computer science with a significant contribution to the subfields of artificial intelligence, computer vision and pattern recognition, computer networks and communications, information systems, and statistical and nonlinear physics.

The main topics covered in their work include advanced graph neural networks, graph theory and algorithms, stochastic gradient optimization techniques, topic modeling, domain adaptation and few-shot learning, cloud computing and resource management, and recommender systems and techniques.

Cheng has published extensively, with frequent appearances in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the ACM on Management of Data
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the VLDB Endowment
  • IEEE Transactions on Parallel and Distributed Systems

Selected recent papers include works such as:

  • ByteGNN, 2022, Proceedings of the VLDB Endowment
  • Measuring and Improving the Use of Graph Information in Graph Neural Networks, 2022, arXiv (Cornell University)
  • Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs, 2022, arXiv (Cornell University)
  • Rethinking Graph Regularization for Graph Neural Networks, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Elastic Deep Learning in Multi-Tenant GPU Clusters, 2021, IEEE Transactions on Parallel and Distributed Systems

Frequent co-authors appearing alongside Cheng include:

  • Xiao Yan
  • Yongqiang Chen
  • Binghui Xie
  • Bo Han
  • Kaiwen Zhou

Best Publications

  • Truss decomposition in massive networks

    Jia Wang;James Cheng

  • K-isomorphism: privacy preserving network publication against structural attacks

    James Cheng;Ada Wai-chee Fu;Jia Liu

  • A model-based approach to attributed graph clustering

    Zhiqiang Xu;Yiping Ke;Yi Wang;Hong Cheng

  • Fg-index: towards verification-free query processing on graph databases

    James Cheng;Yiping Ke;Wilfred Ng;An Lu

  • Path problems in temporal graphs

    Huanhuan Wu;James Cheng;Silu Huang;Yiping Ke

  • Efficient core decomposition in massive networks

    James Cheng;Yiping Ke;Shumo Chu;M. Tamer Ozsu

  • Blogel: a block-centric framework for distributed computation on real-world graphs

    Da Yan;James Cheng;Yi Lu;Wilfred Ng

  • Large-scale distributed graph computing systems: an experimental evaluation

    Yi Lu;James Cheng;Da Yan;Huanhuan Wu

  • Triangle listing in massive networks and its applications

    Shumo Chu;James Cheng

  • Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications

    Fanhua Shang;James Cheng;Yuanyuan Liu;Zhi-Quan Luo

  • A survey on algorithms for mining frequent itemsets over data streams

    James Cheng;Yiping Ke;Wilfred Ng

  • Finding maximal cliques in massive networks

    James Cheng;Yiping Ke;Ada Wai-Chee Fu;Jeffrey Xu Yu

  • Fast algorithms for maximal clique enumeration with limited memory

    James Cheng;Linhong Zhu;Yiping Ke;Shumo Chu

  • Xqzip: Querying compressed XML using structural indexing

    James Cheng;Wilfred Ng

  • Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation

    Da Yan;James Cheng;Yi Lu;Wilfred Ng

  • Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data

    Yuanyuan Liu;Fanhua Shang;Licheng Jiao;James Cheng

  • Finding maximal cliques in massive networks by H*-graph

    James Cheng;Yiping Ke;Ada Wai-Chee Fu;Jeffrey Xu Yu

  • Efficient Algorithms for Temporal Path Computation

    Huanhuan Wu;James Cheng;Yiping Ke;Silu Huang

  • Pregel algorithms for graph connectivity problems with performance guarantees

    Da Yan;James Cheng;Kai Xing;Yi Lu

  • Reachability and time-based path queries in temporal graphs

    Huanhuan Wu;Yuzhen Huang;James Cheng;Jinfeng Li

  • Measuring and Improving the Use of Graph Information in Graph Neural Networks

    Yifan Hou;Jian Zhang;James Cheng;Kaili Ma

Frequent Co-Authors

Wilfred Ng
Wilfred Ng Hong Kong University of Science and Technology
Yiping Ke
Yiping Ke Chinese University of Hong Kong
Ada Wai-Chee Fu
Ada Wai-Chee Fu Chinese University of Hong Kong
Jeffrey Xu Yu
Jeffrey Xu Yu Chinese University of Hong Kong
Licheng Jiao
Licheng Jiao Xidian University
M. Tamer Özsu
M. Tamer Özsu University of Waterloo
John C. S. Lui
John C. S. Lui Chinese University of Hong Kong
Xiaokui Xiao
Xiaokui Xiao National University of Singapore
Zhou Zhao
Zhou Zhao Zhejiang University
Zhi-Quan Luo
Zhi-Quan Luo Chinese University of Hong Kong, Shenzhen

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