Leonardo Neves
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Signal Processing top 10%
- Statistical and Nonlinear Physics top 10%
- Topics
- Topic Modeling (13 papers)Natural Language Processing Techniques (7 papers)Recommender Systems and Techniques (4 papers)
- Partner nations
- United StatesBrazilChina
In The Last Decade
Leonardo Neves
25 papers receiving 623 citations
Hit Papers
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 511
- Computer Vision and Pattern Recognition 171
- Information Systems 86
- Signal Processing 53
- Statistical and Nonlinear Physics 50
Countries citing papers authored by Leonardo Neves
This map shows the geographic impact of Leonardo Neves'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 Leonardo Neves with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonardo Neves more than expected).
Fields of papers citing papers by Leonardo Neves
This network shows the impact of papers produced by Leonardo Neves. 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 Leonardo Neves. The network helps show where Leonardo Neves may publish in the future.
Co-authorship network of co-authors of Leonardo Neves
This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Neves. A scholar is included among the top collaborators of Leonardo Neves 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 Leonardo Neves. Leonardo Neves 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 | 3 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 27 | |
| 7 | 23 | |
| 8 | Data Augmentation for Graph Neural Networksbreakdown → | 212 |
| 9 | 7 | |
| 10 | 4 | |
| 11 | 7 | |
| 12 | 1 | |
| 13 | A Caption Is Worth A Thousand Images: Investigating Image Captions for Multimodal Named Entity Recognition. | 3 |
| 14 | 8 | |
| 15 | 24 | |
| 16 | 7 | |
| 17 | 49 | |
| 18 | 173 | |
| 19 | 3 | |
| 20 | Integrating Domain-Data Steering with Code-Profiling Tools to Debug Data-Intensive Workflows. | 3 |
About Leonardo Neves
Leonardo Neves is a scholar working on Otorhinolaryngology, Complementary and Manual Therapy and Artificial Intelligence, having authored 26 papers that have together received 642 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (7 papers) and Recommender Systems and Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (511 citations), Computer Vision and Pattern Recognition (171 citations) and Signal Processing (53 citations). Leonardo Neves has collaborated with scholars based in United States, Brazil and China. Frequent co-authors include Vı́tor Carvalho, Neil Shah, Yozen Liu, Heng Ji, Di Lu, Meng Jiang, Oliver J. Woodford, Ning Zhang, Tong Zhao and Yun Wang. Their work appears in journals such as Computers in Human Behavior, Future Generation Computer Systems and Clinics.
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.