Guodong Ren
- Statistical and Nonlinear Physics top 0.5%
- Cognitive Neuroscience top 2%
- Computer Networks and Communications top 2%
- Electrical and Electronic Engineering top 10%
- Cellular and Molecular Neuroscience top 5%
- Co-authors
- Jun MaChunni WangYing XuXinlin SongMi LvJun TangFeifei YangFuqiang Wu
- Topics
- stochastic dynamics and bifurcation (31 papers)Neural dynamics and brain function (28 papers)Nonlinear Dynamics and Pattern Formation (13 papers)
- Cited by
- Statistical and Nonlinear PhysicsCognitive NeuroscienceComputer Networks and Communications
- Partner nations
- ChinaSaudi ArabiaUnited States
In The Last Decade
Guodong Ren
36 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 56
- Statistical and Nonlinear Physics 1.5k
- Cognitive Neuroscience 1.2k
- Computer Networks and Communications 730
- Electrical and Electronic Engineering 545
- Cellular and Molecular Neuroscience 295
Countries citing papers authored by Guodong Ren
This map shows the geographic impact of Guodong Ren'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 Guodong Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guodong Ren more than expected).
Fields of papers citing papers by Guodong Ren
This network shows the impact of papers produced by Guodong Ren. 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 Guodong Ren. The network helps show where Guodong Ren may publish in the future.
Co-authorship network of co-authors of Guodong Ren
This figure shows the co-authorship network connecting the top 25 collaborators of Guodong Ren. A scholar is included among the top collaborators of Guodong Ren 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 Guodong Ren. Guodong Ren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 9 | |
| 3 | 0 | |
| 4 | 14 | |
| 5 | 10 | |
| 6 | 45 | |
| 7 | 32 | |
| 8 | 12 | |
| 9 | 16 | |
| 10 | 27 | |
| 11 | 18 | |
| 12 | 21 | |
| 13 | 55 | |
| 14 | 65 | |
| 15 | 116 | |
| 16 | 48 | |
| 17 | 92 | |
| 18 | Model of electrical activity in a neuron under magnetic flow effectbreakdown → | 441 |
| 19 | 30 | |
| 20 | 15 |
About Guodong Ren
Guodong Ren is a scholar working on Statistical and Nonlinear Physics, Cognitive Neuroscience and Cellular and Molecular Neuroscience, having authored 37 papers that have together received 1.7k indexed citations. Recurring topics across this work include stochastic dynamics and bifurcation (31 papers), Neural dynamics and brain function (28 papers) and Nonlinear Dynamics and Pattern Formation (13 papers). The work is most often cited by research in Statistical and Nonlinear Physics (1.5k citations), Cognitive Neuroscience (1.2k citations) and Computer Networks and Communications (730 citations). Guodong Ren has collaborated with scholars based in China, Saudi Arabia and United States. Frequent co-authors include Jun Ma, Chunni Wang, Ying Xu, Xinlin Song, Mi Lv, Jun Tang, Feifei Yang, Fuqiang Wu, Ping Zhou and Tasawar Hayat. Their work appears in journals such as PLoS ONE, Journal of Theoretical Biology and Physics Letters A.
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.