Xiangnan Kong
- Artificial Intelligence top 0.5%
- Statistical and Nonlinear Physics top 0.5%
- Information Systems top 1%
- Molecular Biology
- Computer Vision and Pattern Recognition top 2%
- Topics
- Advanced Graph Neural Networks (27 papers)Complex Network Analysis Techniques (27 papers)Text and Document Classification Technologies (17 papers)
- Partner nations
- United StatesChinaSaudi Arabia
In The Last Decade
Xiangnan Kong
128 papers receiving 3.2k citations
Peers
Comparison fields: 5 of 158
- Artificial Intelligence 1.8k
- Statistical and Nonlinear Physics 871
- Information Systems 640
- Molecular Biology 629
- Computer Vision and Pattern Recognition 492
Countries citing papers authored by Xiangnan Kong
This map shows the geographic impact of Xiangnan Kong'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 Xiangnan Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiangnan Kong more than expected).
Fields of papers citing papers by Xiangnan Kong
This network shows the impact of papers produced by Xiangnan Kong. 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 Xiangnan Kong. The network helps show where Xiangnan Kong may publish in the future.
Co-authorship network of co-authors of Xiangnan Kong
This figure shows the co-authorship network connecting the top 25 collaborators of Xiangnan Kong. A scholar is included among the top collaborators of Xiangnan Kong 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 Xiangnan Kong. Xiangnan Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 9 | |
| 4 | 41 | |
| 5 | 11 | |
| 6 | Deep Learning Strategies for Automatic Detection of Medication and Adverse Drug Events from Electronic Health Records. | 1 |
| 7 | Clustering Uncertain Graphs with Node Attributes. | 3 |
| 8 | 3 | |
| 9 | 7 | |
| 10 | 6 | |
| 11 | 15 | |
| 12 | 15 | |
| 13 | 15 | |
| 14 | 17 | |
| 15 | The effect of intensity-modulated radiotherapy versus conventional radiotherapy on quality of life in patients with nasopharyngeal cancer: a cross-sectional study. | 5 |
| 16 | 14 | |
| 17 | 8 | |
| 18 | 9 | |
| 19 | 35 | |
| 20 | 204 |
About Xiangnan Kong
Xiangnan Kong is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 132 papers that have together received 3.3k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (27 papers), Complex Network Analysis Techniques (27 papers) and Text and Document Classification Technologies (17 papers). The work is most often cited by research in Statistical and Nonlinear Physics (871 citations), Computational Mathematics (33 citations) and Artificial Intelligence (1.8k citations). Xiangnan Kong has collaborated with scholars based in United States, China and Saudi Arabia. Frequent co-authors include Philip S. Yu, Qifeng Yang, Jiawei Zhang, John Boaz Lee, Ryan A. Rossi, Chuan Shi, Bin Wu, Sihong Xie, Bruce G. Haffty and Meena S. Moran. Their work appears in journals such as PLoS ONE, Gene and European Journal of Cancer.
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.