Liang Gou
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Statistical and Nonlinear Physics top 2%
- Information Systems top 5%
- Sociology and Political Science top 10%
- Co-authors
- Xiaolong ZhangAravind SankarYanhong WuWei ZhangHao YangHan‐Wei ShenJunpeng WangC. Lee Giles
- Topics
- Complex Network Analysis Techniques (13 papers)Data Visualization and Analytics (10 papers)Advanced Neural Network Applications (7 papers)
- Cited by
- Statistical and Nonlinear PhysicsArtificial IntelligenceComputer Vision and Pattern Recognition
- Journals
- International Journal of Computer VisionIEEE Transactions on Visualization and Computer GraphicsIEEE Computer Graphics and Applications
- Partner nations
- United StatesGermanyIndia
In The Last Decade
Liang Gou
40 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 709
- Computer Vision and Pattern Recognition 435
- Statistical and Nonlinear Physics 294
- Information Systems 242
- Sociology and Political Science 115
Countries citing papers authored by Liang Gou
This map shows the geographic impact of Liang Gou'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 Liang Gou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang Gou more than expected).
Fields of papers citing papers by Liang Gou
This network shows the impact of papers produced by Liang Gou. 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 Liang Gou. The network helps show where Liang Gou may publish in the future.
Co-authorship network of co-authors of Liang Gou
This figure shows the co-authorship network connecting the top 25 collaborators of Liang Gou. A scholar is included among the top collaborators of Liang Gou 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 Liang Gou. Liang Gou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 4 | |
| 7 | 5 | |
| 8 | 33 | |
| 9 | 0 | |
| 10 | DySAT Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks | 26 |
| 11 | DySATbreakdown → | 305 |
| 12 | 83 | |
| 13 | 102 | |
| 14 | 61 | |
| 15 | 47 | |
| 16 | 6 | |
| 17 | 21 | |
| 18 | 9 | |
| 19 | 90 | |
| 20 | 14 |
About Liang Gou
Liang Gou is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 43 papers that have together received 1.2k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (13 papers), Data Visualization and Analytics (10 papers) and Advanced Neural Network Applications (7 papers). The work is most often cited by research in Statistical and Nonlinear Physics (294 citations), Artificial Intelligence (709 citations) and Computer Vision and Pattern Recognition (435 citations). Liang Gou has collaborated with scholars based in United States, Germany and India. Frequent co-authors include Xiaolong Zhang, Aravind Sankar, Yanhong Wu, Wei Zhang, Hao Yang, Han‐Wei Shen, Junpeng Wang, C. Lee Giles, Hung‐Hsuan Chen and Michelle X. Zhou. Their work appears in journals such as International Journal of Computer Vision, IEEE Transactions on Visualization and Computer Graphics and IEEE Computer Graphics and Applications.
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.