Lichao Sun
- Artificial Intelligence top 0.5%
- Privacy-Preserving Technologies in Data 23
- Topic Modeling 19
- Adversarial Robustness in Machine Learning 19
- Advanced Graph Neural Networks 10
- Natural Language Processing Techniques 8
- Cryptography and Data Security 6
- Health Informatics top 2%
- Signal Processing top 1%
- Advanced Malware Detection Techniques 8
- Software top 2%
- Information Systems top 1%
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- Complex Network Analysis Techniques 10
- Co-authors
- Philip S. YuLingjuan LyuChandra ThapaLifang HeM.A.P. ChamikaraSeyit CamtepeWitawas Srisa‐anQiben Yan
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (5 papers)IEEE Transactions on Industrial Informatics (4 papers)ACM Transactions on Intelligent Systems and Technology (3 papers)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Lichao Sun
86 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 155
- Artificial Intelligence 2.3k
- Health Informatics 81
- Signal Processing 616
- Software 168
- Information Systems 727
Countries citing papers authored by Lichao Sun
This map shows the geographic impact of Lichao Sun'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 Lichao Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lichao Sun more than expected).
Fields of papers citing papers by Lichao Sun
This network shows the impact of papers produced by Lichao Sun. 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 Lichao Sun. The network helps show where Lichao Sun may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lichao Sun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 2 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 16 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 10 | |
| 10 | 2024 | 3 | |
| 11 | 2024 | 45 | |
| 12 | 2023 | 8 | |
| 13 | 2022 | 16 | |
| 14 | 2022 | 27 | |
| 15 | A Survey on Text Classification: From Traditional to Deep Learningbreakdown → | 2022 | 218 |
| 16 | 2021 | 5 | |
| 17 | 2021 | 56 | |
| 18 | 2021 | 49 | |
| 19 | A Text Classification Survey: From Shallow to Deep Learning | 2020 | 7 |
| 20 | Multi-Round Influence Maximization (Extended Version). | 2018 | 1 |
About Lichao Sun
Lichao Sun is a scholar working on Artificial Intelligence, Computational Mathematics and Health Informatics, having authored 99 papers that have together received 3.6k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (23 papers), Topic Modeling (19 papers), Adversarial Robustness in Machine Learning (19 papers), Advanced Graph Neural Networks (10 papers), Complex Network Analysis Techniques (10 papers), Natural Language Processing Techniques (8 papers), Advanced Malware Detection Techniques (8 papers) and Cryptography and Data Security (6 papers). The work is most often cited by research in Artificial Intelligence (2.3k citations), Health Informatics (81 citations) and Signal Processing (616 citations). Lichao Sun has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Philip S. Yu, Lingjuan Lyu, Chandra Thapa, Lifang He, M.A.P. Chamikara, Seyit Camtepe, Witawas Srisa‐an, Qiben Yan, Zhiqiang Li and Heng Ye. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, ACM Transactions on Intelligent Systems and Technology, ACM Computing Surveys and Aging.
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.