Gianni Barlacchi
- Transportation top 1%
- Human Mobility and Location-Based Analysis 12
- Urban Transport and Accessibility 4
- Building and Construction top 2%
- Traffic Prediction and Management Techniques 4
- Signal Processing top 10%
- Automotive Engineering top 10%
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- Topic Modeling 10
- Natural Language Processing Techniques 8
- Speech and dialogue systems 2
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- Multimodal Machine Learning Applications 5
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- Data-Driven Disease Surveillance 4
- Co-authors
- Bruno LepriLuca PappalardoMassimiliano LucaFilippo SiminiGiovanni Luca TorrisiMarco De NadaiRoberto LarcherFabrizio Antonelli
- Journals
- EPJ Data Science (1 paper)IEEE Transactions on Intelligent Transportation Systems (1 paper)Nature Communications (1 paper)
- Partner nations
- ItalyUnited StatesUnited Kingdom
In The Last Decade
Gianni Barlacchi
23 papers receiving 827 citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Transportation 494
- Building and Construction 293
- Computer Networks and Communications 158
- Signal Processing 70
- Automotive Engineering 61
Countries citing papers authored by Gianni Barlacchi
This map shows the geographic impact of Gianni Barlacchi'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 Gianni Barlacchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gianni Barlacchi more than expected).
Fields of papers citing papers by Gianni Barlacchi
This network shows the impact of papers produced by Gianni Barlacchi. 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 Gianni Barlacchi. The network helps show where Gianni Barlacchi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gianni Barlacchi, 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 | A survey on deep learning for human mobilitybreakdown → | 2023 | 122 |
| 2 | 2023 | 2 | |
| 3 | 2023 | 1 | |
| 4 | 2022 | 2 | |
| 5 | 2022 | 2 | |
| 6 | 2022 | 3 | |
| 7 | 2022 | 3 | |
| 8 | 2022 | 37 | |
| 9 | 2021 | 147 | |
| 10 | 2021 | 3 | |
| 11 | 2019 | 11 | |
| 12 | 2019 | 3 | |
| 13 | 2018 | 6 | |
| 14 | Exploiting Deep Neural Networks for Tweet-based Emoji Prediction | 2018 | 2 |
| 15 | 2017 | 39 | |
| 16 | A multi-scale approach to data-driven mass migration analysis | 2016 | 10 |
| 17 | A multi-source dataset of urban life in the city of Milan and the Province of Trentinobreakdown → | 2015 | 341 |
| 18 | 2015 | 5 | |
| 19 | 2015 | 8 | |
| 20 | 2014 | 7 |
About Gianni Barlacchi
Gianni Barlacchi is a scholar working on Transportation, Artificial Intelligence and Building and Construction, having authored 23 papers that have together received 849 indexed citations. Recurring topics across this work include Human Mobility and Location-Based Analysis (12 papers), Topic Modeling (10 papers), Natural Language Processing Techniques (8 papers), Multimodal Machine Learning Applications (5 papers), Traffic Prediction and Management Techniques (4 papers), Urban Transport and Accessibility (4 papers), Data-Driven Disease Surveillance (4 papers) and Speech and dialogue systems (2 papers). The work is most often cited by research in Transportation (494 citations), Building and Construction (293 citations) and Computer Networks and Communications (158 citations). Gianni Barlacchi has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include Bruno Lepri, Luca Pappalardo, Massimiliano Luca, Filippo Simini, Giovanni Luca Torrisi, Marco De Nadai, Roberto Larcher, Fabrizio Antonelli, Alex Pentland and Alessandro Vespignani. Their work appears in journals such as EPJ Data Science, IEEE Transactions on Intelligent Transportation Systems, Nature Communications, Scientific Data and Machine Learning.
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.