Simone Romano
- Artificial Intelligence top 5%
- Advanced Clustering Algorithms Research 5
- Neural Networks and Applications 2
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- Complex Network Analysis Techniques 4
- Signal Processing top 10%
- Data Management and Algorithms 3
- Time Series Analysis and Forecasting 2
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- Advanced Statistical Methods and Models 2
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- Complex Systems and Time Series Analysis 2
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- Sensory Analysis and Statistical Methods 2
- Co-authors
- James BaileyNguyễn Xuân VinhJeffrey ChanKarin VerspoorJames C. BezdekYisen WangXingjun MaSarah Erfani
- Cited by
- Artificial IntelligenceStatistical and Nonlinear PhysicsComputer Vision and Pattern Recognition
In The Last Decade
Simone Romano
11 papers receiving 367 citations
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 259
- Statistical and Nonlinear Physics 57
- Computer Vision and Pattern Recognition 94
- Signal Processing 44
- Computational Theory and Mathematics 29
Countries citing papers authored by Simone Romano
This map shows the geographic impact of Simone Romano'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 Simone Romano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simone Romano more than expected).
Fields of papers citing papers by Simone Romano
This network shows the impact of papers produced by Simone Romano. 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 Simone Romano. The network helps show where Simone Romano may publish in the future.
Co-authorship network
The 13 scholars most cited alongside Simone Romano, 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 | 2020 | 59 | |
| 2 | 2017 | 13 | |
| 3 | 2017 | 8 | |
| 4 | 2016 | 47 | |
| 5 | 2016 | 33 | |
| 6 | 2016 | 40 | |
| 7 | 2016 | 7 | |
| 8 | 2016 | 43 | |
| 9 | Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance | 2014 | 42 |
| 10 | 2014 | 82 | |
| 11 | 2014 | 7 |
About Simone Romano
Simone Romano is a scholar working on Signal Processing, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 11 papers that have together received 381 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (5 papers), Complex Network Analysis Techniques (4 papers), Data Management and Algorithms (3 papers), Neural Networks and Applications (2 papers), Time Series Analysis and Forecasting (2 papers), Advanced Statistical Methods and Models (2 papers), Complex Systems and Time Series Analysis (2 papers) and Sensory Analysis and Statistical Methods (2 papers). The work is most often cited by research in Artificial Intelligence (259 citations), Statistical and Nonlinear Physics (57 citations) and Computer Vision and Pattern Recognition (94 citations). Simone Romano has collaborated with scholars based in Australia, China and Canada. Frequent co-authors include James Bailey, Nguyễn Xuân Vinh, Jeffrey Chan, Karin Verspoor, James C. Bezdek, Yisen Wang, Xingjun Ma, Sarah Erfani, Kotagiri Ramamohanarao and Jian Pei. Their work appears in journals such as IEEE Transactions on Fuzzy Systems, Data Mining and Knowledge Discovery, Journal of Machine Learning Research, Machine Learning and Pattern Recognition.
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.