Weijia Shi
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Computer Networks and Communications top 10%
- Management Science and Operations Research
- Computer Vision and Pattern Recognition
- Co-authors
- Shaowei WangMuhao ChenChonggang WangYizhou SunXuelu ChenCarlo ZanioloKai-Wei ChangPei Zhou
- Topics
- Natural Language Processing Techniques (9 papers)Topic Modeling (8 papers)Advanced MIMO Systems Optimization (4 papers)
- Journals
- IEEE Transactions on CommunicationsProceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Weijia Shi
16 papers receiving 318 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 201
- Electrical and Electronic Engineering 90
- Computer Networks and Communications 78
- Management Science and Operations Research 30
- Computer Vision and Pattern Recognition 29
Countries citing papers authored by Weijia Shi
This map shows the geographic impact of Weijia 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 Weijia Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weijia Shi more than expected).
Fields of papers citing papers by Weijia Shi
This network shows the impact of papers produced by Weijia 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 Weijia Shi. The network helps show where Weijia Shi may publish in the future.
Co-authorship network of co-authors of Weijia Shi
This figure shows the co-authorship network connecting the top 25 collaborators of Weijia Shi. A scholar is included among the top collaborators of Weijia 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 Weijia Shi. Weijia 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 | 5 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 58 | |
| 5 | 1 | |
| 6 | 28 | |
| 7 | 5 | |
| 8 | 19 | |
| 9 | Cross-lingual Entity Alignment for Knowledge Graphs with Incidental Supervision from Free Text | 1 |
| 10 | 41 | |
| 11 | 1 | |
| 12 | 13 | |
| 13 | 68 | |
| 14 | 69 | |
| 15 | 12 | |
| 16 | 4 | |
| 17 | 10 |
About Weijia Shi
Weijia Shi is a scholar working on Artificial Intelligence, Computer Networks and Communications and Cultural Studies, having authored 17 papers that have together received 336 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (8 papers) and Advanced MIMO Systems Optimization (4 papers). The work is most often cited by research in Artificial Intelligence (201 citations), Health Informatics (6 citations) and Computer Networks and Communications (78 citations). Weijia Shi has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Shaowei Wang, Muhao Chen, Chonggang Wang, Yizhou Sun, Xuelu Chen, Carlo Zaniolo, Kai-Wei Chang, Pei Zhou, Dan Roth and Luke Zettlemoyer. Their work appears in journals such as IEEE Transactions on Communications and Proceedings of the AAAI Conference on Artificial Intelligence.
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.