Ling-Wei Kong
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 5%
- Computer Networks and Communications top 10%
- Cognitive Neuroscience
- Electrical and Electronic Engineering
- Co-authors
- Ying‐Cheng LaiHuawei FanCelso GrebogiXingang WangMulugeta HaileBryan GlazRui XiaoZhongkui Sun
- Topics
- Neural Networks and Reservoir Computing (10 papers)Nonlinear Dynamics and Pattern Formation (7 papers)Neural dynamics and brain function (4 papers)
- Cited by
- Statistical and Nonlinear PhysicsArtificial IntelligenceComputer Networks and Communications
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Ling-Wei Kong
16 papers receiving 396 citations
Peers
Comparison fields: 5 of 58
- Artificial Intelligence 241
- Statistical and Nonlinear Physics 151
- Computer Networks and Communications 123
- Cognitive Neuroscience 98
- Electrical and Electronic Engineering 86
Countries citing papers authored by Ling-Wei Kong
This map shows the geographic impact of Ling-Wei 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 Ling-Wei Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ling-Wei Kong more than expected).
Fields of papers citing papers by Ling-Wei Kong
This network shows the impact of papers produced by Ling-Wei 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 Ling-Wei Kong. The network helps show where Ling-Wei Kong may publish in the future.
Co-authorship network of co-authors of Ling-Wei Kong
This figure shows the co-authorship network connecting the top 25 collaborators of Ling-Wei Kong. A scholar is included among the top collaborators of Ling-Wei 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 Ling-Wei Kong. Ling-Wei 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 | 4 | |
| 3 | 8 | |
| 4 | 17 | |
| 5 | 22 | |
| 6 | 4 | |
| 7 | 35 | |
| 8 | 38 | |
| 9 | 16 | |
| 10 | 3 | |
| 11 | 37 | |
| 12 | 54 | |
| 13 | 11 | |
| 14 | 28 | |
| 15 | 93 | |
| 16 | 12 | |
| 17 | 18 |
About Ling-Wei Kong
Ling-Wei Kong is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computer Networks and Communications, having authored 17 papers that have together received 400 indexed citations. Recurring topics across this work include Neural Networks and Reservoir Computing (10 papers), Nonlinear Dynamics and Pattern Formation (7 papers) and Neural dynamics and brain function (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (151 citations), Artificial Intelligence (241 citations) and Computer Networks and Communications (123 citations). Ling-Wei Kong has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Ying‐Cheng Lai, Huawei Fan, Celso Grebogi, Xingang Wang, Mulugeta Haile, Bryan Glaz, Rui Xiao, Zhongkui Sun, Yang Weng and Gene A. Brewer. Their work appears in journals such as Nature Communications, Journal of The Royal Society Interface and National Science Review.
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.