Davide Mottin
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Semantic Web and Ontologies
- Topic Modeling
Papers in
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- Advanced Graph Neural Networks 14
- Semantic Web and Ontologies 5
- Algorithms and Data Compression 5
- Topic Modeling 5
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- Graph Theory and Algorithms 13
- Co-authors
- Yannis Velegrakis (18 shared papers)Themis Palpanas (16 shared papers)Matteo Lissandrini (17 shared papers)Emmanuel Müller (5 shared papers)Gautam Das (3 shared papers)Senjuti Basu Roy (3 shared papers)Francesco Gullo (2 shared papers)Francesco Bonchi (1 shared paper)
In The Last Decade
Davide Mottin
35 papers receiving 387 citations
Peers
Comparison fields: 5 of 34
- Signal Processing 126
- Artificial Intelligence 274
- Computer Vision and Pattern Recognition 147
- Management Science and Operations Research 69
- Computer Networks and Communications 102
Countries citing papers authored by Davide Mottin
This map shows the geographic impact of Davide Mottin'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 Davide Mottin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davide Mottin more than expected).
Fields of papers citing papers by Davide Mottin
This network shows the impact of papers produced by Davide Mottin. 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 Davide Mottin. The network helps show where Davide Mottin may publish in the future.
Co-authors
The 25 scholars most cited alongside Davide Mottin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 50 | |
| 2 | 2016 | 37 | |
| 3 | 2013 | 32 | |
| 4 | 2017 | 26 | |
| 5 | 2021 | 26 | |
| 6 | 2015 | 23 | |
| 7 | 2019 | 20 | |
| 8 | 2020 | 19 | |
| 9 | 2020 | 17 | |
| 10 | 2014 | 16 | |
| 11 | 2018 | 13 | |
| 12 | 2014 | 11 | |
| 13 | 2015 | 10 | |
| 14 | 2013 | 10 | |
| 15 | 2019 | 7 | |
| 16 | 2018 | 7 | |
| 17 | 2018 | 7 | |
| 18 | 2019 | 6 | |
| 19 | 2018 | 6 | |
| 20 | 2021 | 5 |
About Davide Mottin
Davide Mottin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Signal Processing and Information Systems, having authored 42 papers that have together received 400 indexed citations. Recurring topics across this work include Data Management and Algorithms (14 papers), Advanced Graph Neural Networks (14 papers), Graph Theory and Algorithms (13 papers), Advanced Database Systems and Queries (10 papers), Complex Network Analysis Techniques (6 papers), Semantic Web and Ontologies (5 papers), Algorithms and Data Compression (5 papers) and Topic Modeling (5 papers). The work is most often cited by research in Signal Processing (126 citations), Artificial Intelligence (274 citations), Computer Vision and Pattern Recognition (147 citations), Management Science and Operations Research (69 citations) and Computer Networks and Communications (102 citations). Davide Mottin has collaborated with scholars based in Denmark, Italy and France. Frequent co-authors include Yannis Velegrakis, Themis Palpanas, Matteo Lissandrini, Emmanuel Müller, Gautam Das, Senjuti Basu Roy, Francesco Gullo, Francesco Bonchi, Panagiotis Karras and Torben Bach Pedersen. Their work appears in journals such as Proceedings of the VLDB Endowment, The VLDB Journal, Distributed and Parallel Databases, Machine Learning and Machine Learning Science and Technology.
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.