Andrea Carbone
- Human-Computer Interaction top 5%
- Cognitive Neuroscience
- Social Psychology
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Co-authors
- Thierry BaccinoSimone BenedettoMarco PedrottiVéronique Drai-ZerbibEdwige PissalouxFiora PirriAnna BelardinelliAlberto Finzi
- Topics
- Gaze Tracking and Assistive Technology (11 papers)Visual Attention and Saliency Detection (5 papers)Robotic Path Planning Algorithms (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaComputers in Human BehaviorPattern Recognition Letters
- Partner nations
- ItalyFranceSwitzerland
In The Last Decade
Andrea Carbone
18 papers receiving 268 citations
Peers
Comparison fields: 5 of 63
- Human-Computer Interaction 116
- Cognitive Neuroscience 85
- Social Psychology 79
- Computer Vision and Pattern Recognition 71
- Artificial Intelligence 31
Countries citing papers authored by Andrea Carbone
This map shows the geographic impact of Andrea Carbone'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 Andrea Carbone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrea Carbone more than expected).
Fields of papers citing papers by Andrea Carbone
This network shows the impact of papers produced by Andrea Carbone. 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 Andrea Carbone. The network helps show where Andrea Carbone may publish in the future.
Co-authorship network of co-authors of Andrea Carbone
This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Carbone. A scholar is included among the top collaborators of Andrea Carbone 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 Andrea Carbone. Andrea Carbone is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 16 | |
| 4 | 42 | |
| 5 | 111 | |
| 6 | 3 | |
| 7 | 9 | |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 32 | |
| 11 | 1 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 21 | |
| 15 | 23 | |
| 16 | 2 | |
| 17 | 3 | |
| 18 | 8 | |
| 19 | 3 |
About Andrea Carbone
Andrea Carbone is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Urban Studies, having authored 19 papers that have together received 284 indexed citations. Recurring topics across this work include Gaze Tracking and Assistive Technology (11 papers), Visual Attention and Saliency Detection (5 papers) and Robotic Path Planning Algorithms (3 papers). The work is most often cited by research in Human-Computer Interaction (116 citations), Cognitive Neuroscience (85 citations) and Social Psychology (79 citations). Andrea Carbone has collaborated with scholars based in Italy, France and Switzerland. Frequent co-authors include Thierry Baccino, Simone Benedetto, Marco Pedrotti, Véronique Drai-Zerbib, Edwige Pissaloux, Fiora Pirri, Anna Belardinelli, Alberto Finzi, Andrea Orlandini and Giuseppe Ugazio. Their work appears in journals such as SHILAP Revista de lepidopterología, Computers in Human Behavior and Pattern Recognition Letters.
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.