Suhang Wang
- Artificial Intelligence top 0.1%
- Advanced Graph Neural Networks 46
- Topic Modeling 32
- Explainable Artificial Intelligence (XAI) 14
- Domain Adaptation and Few-Shot Learning 12
- Information Systems top 0.05%
- Recommender Systems and Techniques 27
- Spam and Phishing Detection 15
- Signal Processing top 0.5%
- Sociology and Political Science top 0.2%
- Misinformation and Its Impacts 15
- Statistical and Nonlinear Physics top 0.5%
- Complex Network Analysis Techniques 17
Suhang Wang
153 papers receiving 9.6k citations
Hit Papers
Peers
Comparison fields: 5 of 195
- Artificial Intelligence 5.5k
- Information Systems 3.8k
- Signal Processing 1.2k
- Sociology and Political Science 4.0k
- Statistical and Nonlinear Physics 1.1k
Countries citing papers authored by Suhang Wang
This map shows the geographic impact of Suhang 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 Suhang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suhang Wang more than expected).
Fields of papers citing papers by Suhang Wang
This network shows the impact of papers produced by Suhang 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 Suhang Wang. The network helps show where Suhang Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Suhang 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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 3 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 4 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 2 | |
| 10 | 2024 | 0 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 7 | |
| 13 | 2023 | 7 | |
| 14 | 2023 | 4 | |
| 15 | 2023 | 1 | |
| 16 | 2023 | 5 | |
| 17 | 2021 | 3 | |
| 18 | 2020 | 52 | |
| 19 | 2019 | 24 | |
| 20 | Feature Selectionbreakdown → | 2017 | 1541 |
About Suhang Wang
Suhang Wang is a scholar working on Artificial Intelligence, Information Systems, Computational Mathematics, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 163 papers that have together received 9.9k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (46 papers), Topic Modeling (32 papers), Recommender Systems and Techniques (27 papers), Complex Network Analysis Techniques (17 papers), Spam and Phishing Detection (15 papers), Misinformation and Its Impacts (15 papers), Explainable Artificial Intelligence (XAI) (14 papers) and Domain Adaptation and Few-Shot Learning (12 papers). The work is most often cited by research in Artificial Intelligence (5.5k citations), Information Systems (3.8k citations), Signal Processing (1.2k citations), Sociology and Political Science (4.0k citations) and Statistical and Nonlinear Physics (1.1k citations). Suhang Wang has collaborated with scholars based in United States, China and Singapore. Frequent co-authors include Kai Shu, Huan Liu, Jiliang Tang, Amy Sliva, Huan Liu, Dongwon Lee, Jundong Li, Fred Morstatter, Kewei Cheng and Robert P. Treviño. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Knowledge and Data Engineering, Information Sciences, Neurocomputing and Knowledge-Based Systems.
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.