Can Wang
Impact in
- Artificial Intelligence top 0.5%
- Advanced Graph Neural Networks
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
Papers in
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- Advanced Graph Neural Networks 27
- Topic Modeling 18
- Domain Adaptation and Few-Shot Learning 11
- Advanced Clustering Algorithms Research 10
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- Recommender Systems and Techniques 40
- Co-authors
- Chun Chen (31 shared papers)Jiajun Bu (29 shared papers)Defang Chen (9 shared papers)Martin Ester (12 shared papers)Longbing Cao (13 shared papers)Feng Yan (6 shared papers)Xiaofei He (6 shared papers)Qihao Shi (13 shared papers)
- Journals
- Knowledge-Based Systems (10 papers)Neurocomputing (7 papers)World Wide Web (4 papers)IEEE Intelligent Systems (4 papers)Expert Systems with Applications (4 papers)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Can Wang
201 papers receiving 3.7k citations
Can Wang's Hit Papers
Peers
Comparison fields: 5 of 187
- Artificial Intelligence 1.6k
- Statistical and Nonlinear Physics 593
- Information Systems 1.0k
- Computer Vision and Pattern Recognition 877
- Signal Processing 219
Countries citing papers authored by Can Wang
This map shows the geographic impact of Can Wang'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 Can Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Can Wang more than expected).
Fields of papers citing papers by Can Wang
This network shows the impact of papers produced by Can Wang. 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 Can Wang. The network helps show where Can Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Can Wang, 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 228 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 187 | |
| 2 | 2010 | 181 | |
| 3 | 2017 | 167 | |
| 4 | 2018 | 154 | |
| 5 | 2016 | 106 | |
| 6 | 2009 | 94 | |
| 7 | 2016 | 73 | |
| 8 | 2015 | 71 | |
| 9 | 2010 | 70 | |
| 10 | 2021 | 68 | |
| 11 | Genetic association of lipids and lipid-lowering drug target genes with non-alcoholic fatty liver disease Hit paper breakdown → | 2023 | 66 |
| 12 | 2019 | 59 | |
| 13 | 2013 | 58 | |
| 14 | 2020 | 53 | |
| 15 | 2021 | 53 | |
| 16 | 2022 | 52 | |
| 17 | 2011 | 50 | |
| 18 | 2014 | 50 | |
| 19 | 2016 | 46 | |
| 20 | 2022 | 45 |
About Can Wang
Can Wang is a scholar working on Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 228 papers that have together received 3.7k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (40 papers), Complex Network Analysis Techniques (38 papers), Advanced Graph Neural Networks (27 papers), Opinion Dynamics and Social Influence (18 papers), Topic Modeling (18 papers), Domain Adaptation and Few-Shot Learning (11 papers), Human Mobility and Location-Based Analysis (11 papers) and Advanced Clustering Algorithms Research (10 papers). The work is most often cited by research in Artificial Intelligence (1.6k citations), Statistical and Nonlinear Physics (593 citations), Information Systems (1.0k citations), Computer Vision and Pattern Recognition (877 citations) and Signal Processing (219 citations). Can Wang has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Chun Chen, Jiajun Bu, Defang Chen, Martin Ester, Longbing Cao, Feng Yan, Xiaofei He, Qihao Shi, Jian-Ping Mei and Weimin Li. Their work appears in journals such as Knowledge-Based Systems, Neurocomputing, World Wide Web, IEEE Intelligent Systems and Expert Systems with Applications.
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.