X. D. Zhang
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 10%
- Statistical and Nonlinear Physics top 5%
- Molecular Biology
- Topics
- Advanced Graph Neural Networks (18 papers)Complex Network Analysis Techniques (7 papers)Explainable Artificial Intelligence (XAI) (5 papers)
- Cited by
- Artificial IntelligenceStatistical and Nonlinear PhysicsComputer Vision and Pattern Recognition
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Knowledge and Data EngineeringACM Transactions on Intelligent Systems and Technology
- Partner nations
- United StatesChinaSingapore
In The Last Decade
X. D. Zhang
27 papers receiving 549 citations
Hit Papers
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 456
- Information Systems 111
- Computer Vision and Pattern Recognition 109
- Statistical and Nonlinear Physics 109
- Molecular Biology 48
Countries citing papers authored by X. D. Zhang
This map shows the geographic impact of X. D. Zhang'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 X. D. Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites X. D. Zhang more than expected).
Fields of papers citing papers by X. D. Zhang
This network shows the impact of papers produced by X. D. Zhang. 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 X. D. Zhang. The network helps show where X. D. Zhang may publish in the future.
Co-authorship network of co-authors of X. D. Zhang
This figure shows the co-authorship network connecting the top 25 collaborators of X. D. Zhang. A scholar is included among the top collaborators of X. D. Zhang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with X. D. Zhang. X. D. Zhang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 6 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 4 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 7 | |
| 11 | 8 | |
| 12 | 20 | |
| 13 | Learning to Drop: Robust Graph Neural Network via Topological Denoisingbreakdown → | 156 |
| 14 | 11 | |
| 15 | GNNGuard: Defending Graph Neural Networks against Adversarial Attacks | 8 |
| 16 | 31 | |
| 17 | 43 | |
| 18 | 11 | |
| 19 | 6 | |
| 20 | 2 |
About X. D. Zhang
X. D. Zhang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Urban Studies, having authored 30 papers that have together received 557 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (18 papers), Complex Network Analysis Techniques (7 papers) and Explainable Artificial Intelligence (XAI) (5 papers). The work is most often cited by research in Artificial Intelligence (456 citations), Statistical and Nonlinear Physics (109 citations) and Computer Vision and Pattern Recognition (109 citations). X. D. Zhang has collaborated with scholars based in United States, China and Singapore. Frequent co-authors include Wei Cheng, Dongsheng Luo, Jingchao Ni, Bo Zong, Wenchao Yu, Dongkuan Xu, Siyu Huang, Yihang Yin, Haifeng Chen and Suhang Wang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering and ACM Transactions on Intelligent Systems and Technology.
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.