Zhan Shi
- Computer Networks and Communications top 5%
- Artificial Intelligence top 10%
- Information Systems top 5%
- Hardware and Architecture top 5%
- Computer Vision and Pattern Recognition top 10%
- Topics
- Advanced Data Storage Technologies (27 papers)Caching and Content Delivery (24 papers)Cloud Computing and Resource Management (17 papers)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Zhan Shi
54 papers receiving 439 citations
Peers
Comparison fields: 5 of 68
- Computer Networks and Communications 241
- Artificial Intelligence 190
- Information Systems 151
- Hardware and Architecture 103
- Computer Vision and Pattern Recognition 74
Countries citing papers authored by Zhan Shi
This map shows the geographic impact of Zhan Shi'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 Zhan Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhan Shi more than expected).
Fields of papers citing papers by Zhan Shi
This network shows the impact of papers produced by Zhan Shi. 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 Zhan Shi. The network helps show where Zhan Shi may publish in the future.
Co-authorship network of co-authors of Zhan Shi
This figure shows the co-authorship network connecting the top 25 collaborators of Zhan Shi. A scholar is included among the top collaborators of Zhan Shi 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 Zhan Shi. Zhan Shi 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 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 2 | |
| 9 | 20 | |
| 10 | 1 | |
| 11 | 6 | |
| 12 | Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks | 8 |
| 13 | Inductive Two-layer Modeling with Parametric Bregman Transfer. | 0 |
| 14 | Monge blunts Bayes: Hardness Results for Adversarial Training. | 1 |
| 15 | 1 | |
| 16 | 5 | |
| 17 | Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction | 4 |
| 18 | 3 | |
| 19 | 2 | |
| 20 | Disk array performance prediction with CART-MARS hybrid models | 1 |
About Zhan Shi
Zhan Shi is a scholar working on Computer Networks and Communications, Information Systems and Hardware and Architecture, having authored 65 papers that have together received 457 indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (27 papers), Caching and Content Delivery (24 papers) and Cloud Computing and Resource Management (17 papers). The work is most often cited by research in Hardware and Architecture (103 citations), Computer Networks and Communications (241 citations) and Information Systems (151 citations). Zhan Shi has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Dan Feng, Calvin Lin, Akanksha Jain, Xiangru Huang, Hong Jiang, Lingfang Zeng, Shengjie Xu, Ning Li, Kevin Swersky and Milad Hashemi. Their work appears in journals such as Bioinformatics, IEEE Access and IEEE Transactions on Knowledge and Data Engineering.
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.