Federico Errica
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks 9
- Topic Modeling 3
- Bayesian Modeling and Causal Inference 2
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- Complex Network Analysis Techniques 3
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- Graph Theory and Algorithms 3
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- Computational Drug Discovery Methods 3
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- Machine Learning in Materials Science 3
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- Protein Structure and Dynamics 2
Federico Errica
15 papers receiving 313 citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 182
- Statistical and Nonlinear Physics 43
- Computer Vision and Pattern Recognition 49
- Computational Theory and Mathematics 37
- Computational Mathematics 1
Countries citing papers authored by Federico Errica
This map shows the geographic impact of Federico Errica'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 Federico Errica with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Federico Errica more than expected).
Fields of papers citing papers by Federico Errica
This network shows the impact of papers produced by Federico Errica. 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 Federico Errica. The network helps show where Federico Errica may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Federico Errica, 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 | 2025 | 0 | |
| 3 | 2024 | 25 | |
| 4 | 2023 | 1 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 5 | |
| 9 | 2022 | 4 | |
| 10 | 2022 | 19 | |
| 11 | 2021 | 12 | |
| 12 | 2021 | 1 | |
| 13 | 2021 | 2 | |
| 14 | Probabilistic Learning on Graphs via Contextual Architectures | 2020 | 5 |
| 15 | 2020 | 190 | |
| 16 | 2019 | 49 | |
| 17 | 2017 | 2 |
About Federico Errica
Federico Errica is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 17 papers that have together received 322 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (9 papers), Machine Learning in Materials Science (3 papers), Graph Theory and Algorithms (3 papers), Computational Drug Discovery Methods (3 papers), Topic Modeling (3 papers), Complex Network Analysis Techniques (3 papers), Protein Structure and Dynamics (2 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Artificial Intelligence (182 citations), Statistical and Nonlinear Physics (43 citations) and Computer Vision and Pattern Recognition (49 citations). Federico Errica has collaborated with scholars based in Italy, Germany and Japan. Frequent co-authors include Alessio Micheli, Davide Bacciu, Marco Podda, Francesco Alesiani, Mathias Niepert, Viktor Zaverkin, Johannes Kästner, Makoto Takamoto, Marco Giulini and Franco Scarselli. Their work appears in journals such as The Journal of Chemical Physics, PLoS ONE and Neurocomputing.
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.