James Cheng
Impact in
- Computational Mathematics top 0.5%
- Signal Processing top 0.5%
- Data Management and Algorithms
Papers in
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- Advanced Graph Neural Networks 34
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- Graph Theory and Algorithms 52
- Co-authors
- Yiping Ke (30 shared papers)Wilfred Ng (28 shared papers)Ada Wai-Chee Fu (10 shared papers)Shumo Chu (5 shared papers)Da Yan (22 shared papers)Huanhuan Wu (10 shared papers)Yi Lu (7 shared papers)Jia Wang (1 shared paper)
In The Last Decade
James Cheng
142 papers receiving 5.3k citations
James Cheng's Hit Papers
Peers
Comparison fields: 5 of 106
- Computational Mathematics 237
- Signal Processing 1.5k
- Computer Vision and Pattern Recognition 2.3k
- Statistical and Nonlinear Physics 1.3k
- Computer Networks and Communications 1.8k
Countries citing papers authored by James Cheng
This map shows the geographic impact of James Cheng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by James Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Cheng more than expected).
Fields of papers citing papers by James Cheng
This network shows the impact of papers produced by James Cheng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James Cheng. The network helps show where James Cheng may publish in the future.
Co-authors
The 25 scholars most cited alongside James Cheng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 150 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Truss decomposition in massive networks Hit paper breakdown → | 2012 | 298 |
| 2 | 2010 | 269 | |
| 3 | 2012 | 242 | |
| 4 | 2007 | 220 | |
| 5 | 2011 | 205 | |
| 6 | 2014 | 202 | |
| 7 | 2014 | 176 | |
| 8 | 2017 | 135 | |
| 9 | 2011 | 114 | |
| 10 | 2007 | 112 | |
| 11 | 2012 | 110 | |
| 12 | 2014 | 107 | |
| 13 | 2011 | 102 | |
| 14 | 2014 | 98 | |
| 15 | 2013 | 88 | |
| 16 | 2010 | 83 | |
| 17 | 2015 | 78 | |
| 18 | 2016 | 76 | |
| 19 | 2018 | 75 | |
| 20 | 2013 | 72 |
About James Cheng
James Cheng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Information Systems and Signal Processing, having authored 150 papers that have together received 5.4k indexed citations. Recurring topics across this work include Graph Theory and Algorithms (52 papers), Data Management and Algorithms (36 papers), Advanced Graph Neural Networks (34 papers), Cloud Computing and Resource Management (24 papers), Sparse and Compressive Sensing Techniques (21 papers), Advanced Database Systems and Queries (19 papers), Complex Network Analysis Techniques (19 papers) and Caching and Content Delivery (16 papers). The work is most often cited by research in Computational Mathematics (237 citations), Signal Processing (1.5k citations), Computer Vision and Pattern Recognition (2.3k citations), Statistical and Nonlinear Physics (1.3k citations) and Computer Networks and Communications (1.8k citations). James Cheng has collaborated with scholars based in Hong Kong, China and Singapore. Frequent co-authors include Yiping Ke, Wilfred Ng, Ada Wai-Chee Fu, Shumo Chu, Da Yan, Huanhuan Wu, Yi Lu, Jia Wang, Fanhua Shang and Yuanyuan Liu. Their work appears in journals such as Proceedings of the VLDB Endowment, Knowledge and Information Systems, IEEE Transactions on Parallel and Distributed Systems, ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.