Marco De Nadai
- Transportation top 1%
- Human Mobility and Location-Based Analysis 8
- Building and Construction top 5%
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- Generative Adversarial Networks and Image Synthesis 4
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies 2
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- Urban, Neighborhood, and Segregation Studies 3
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- Recommender Systems and Techniques 3
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- Topic Modeling 2
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- Impact of Light on Environment and Health 2
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- Complex Network Analysis Techniques 2
- Co-authors
- Bruno LepriAlex PentlandFabrizio AntonelliGiovanni Luca TorrisiRoberto LarcherAlessandro VespignaniGianni BarlacchiMaarten van Someren
- Journals
- Scientific Reports (4 papers)Machine Learning (1 paper)IEEE Transactions on Multimedia (1 paper)
- Partner nations
- ItalyUnited StatesChina
In The Last Decade
Marco De Nadai
19 papers receiving 756 citations
Hit Papers
Peers
Comparison fields: 5 of 88
- Transportation 312
- Building and Construction 191
- Computer Networks and Communications 149
- Computer Vision and Pattern Recognition 114
- Modeling and Simulation 25
Countries citing papers authored by Marco De Nadai
This map shows the geographic impact of Marco De Nadai'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 Marco De Nadai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco De Nadai more than expected).
Fields of papers citing papers by Marco De Nadai
This network shows the impact of papers produced by Marco De Nadai. 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 Marco De Nadai. The network helps show where Marco De Nadai may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Marco De Nadai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 7 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2022 | 4 | |
| 7 | 2022 | 8 | |
| 8 | 2021 | 50 | |
| 9 | 2021 | 34 | |
| 10 | 2021 | 5 | |
| 11 | 2020 | 53 | |
| 12 | 2020 | 36 | |
| 13 | 2019 | 23 | |
| 14 | 2018 | 33 | |
| 15 | 2018 | 10 | |
| 16 | 2016 | 18 | |
| 17 | 2016 | 37 | |
| 18 | 2016 | 83 | |
| 19 | A multi-source dataset of urban life in the city of Milan and the Province of Trentinobreakdown → | 2015 | 341 |
| 20 | 2015 | 31 |
About Marco De Nadai
Marco De Nadai is a scholar working on Transportation, Modeling and Simulation and Computer Vision and Pattern Recognition, having authored 20 papers that have together received 776 indexed citations. Recurring topics across this work include Human Mobility and Location-Based Analysis (8 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Urban, Neighborhood, and Segregation Studies (3 papers), Recommender Systems and Techniques (3 papers), COVID-19 epidemiological studies (2 papers), Topic Modeling (2 papers), Impact of Light on Environment and Health (2 papers) and Complex Network Analysis Techniques (2 papers). The work is most often cited by research in Transportation (312 citations), Building and Construction (191 citations) and Computer Networks and Communications (149 citations). Marco De Nadai has collaborated with scholars based in Italy, United States and China. Frequent co-authors include Bruno Lepri, Alex Pentland, Fabrizio Antonelli, Giovanni Luca Torrisi, Roberto Larcher, Alessandro Vespignani, Gianni Barlacchi, Maarten van Someren, Nicu Sebe and Marta C. González. Their work appears in journals such as Scientific Reports, Machine Learning and IEEE Transactions on Multimedia.
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.