Fabrizio Frasca
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Statistical and Nonlinear Physics
- Computational Theory and Mathematics
- Information Systems
- Co-authors
- Giorgos BouritsasMichael M. BronsteinStefanos ZafeiriouMatteo MatteucciMarco J. MorelliMarco MasseroliSabina FiciliMichele Leone
- Topics
- Advanced Graph Neural Networks (2 papers)Genomics and Chromatin Dynamics (2 papers)Cardiac electrophysiology and arrhythmias (1 paper)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionStatistical and Nonlinear Physics
- Journals
- Journal of the American College of CardiologyIEEE Transactions on Pattern Analysis and Machine IntelligenceBMC Bioinformatics
- Partner nations
- ItalySwitzerlandUnited Kingdom
In The Last Decade
Fabrizio Frasca
5 papers receiving 139 citations
Hit Papers
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 101
- Computer Vision and Pattern Recognition 50
- Statistical and Nonlinear Physics 27
- Computational Theory and Mathematics 22
- Information Systems 21
Countries citing papers authored by Fabrizio Frasca
This map shows the geographic impact of Fabrizio Frasca'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 Fabrizio Frasca with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabrizio Frasca more than expected).
Fields of papers citing papers by Fabrizio Frasca
This network shows the impact of papers produced by Fabrizio Frasca. 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 Fabrizio Frasca. The network helps show where Fabrizio Frasca may publish in the future.
Co-authorship network of co-authors of Fabrizio Frasca
This figure shows the co-authorship network connecting the top 25 collaborators of Fabrizio Frasca. A scholar is included among the top collaborators of Fabrizio Frasca 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 Fabrizio Frasca. Fabrizio Frasca is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | Improving Graph Neural Network Expressivity via Subgraph Isomorphism Countingbreakdown → | 139 |
| 4 | 1 | |
| 5 | 1 |
About Fabrizio Frasca
Fabrizio Frasca is a scholar working on Statistical and Nonlinear Physics, Cancer Research and Artificial Intelligence, having authored 5 papers that have together received 144 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (2 papers), Genomics and Chromatin Dynamics (2 papers) and Cardiac electrophysiology and arrhythmias (1 paper). The work is most often cited by research in Artificial Intelligence (101 citations), Computer Vision and Pattern Recognition (50 citations) and Statistical and Nonlinear Physics (27 citations). Fabrizio Frasca has collaborated with scholars based in Italy, Switzerland and United Kingdom. Frequent co-authors include Giorgos Bouritsas, Michael M. Bronstein, Stefanos Zafeiriou, Matteo Matteucci, Marco J. Morelli, Marco Masseroli, Sabina Ficili, Michele Leone, Luigi Sciarra and Leonardo Calò. Their work appears in journals such as Journal of the American College of Cardiology, IEEE Transactions on Pattern Analysis and Machine Intelligence and BMC Bioinformatics.
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.