Fernando Silva
Impact in
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- Complex Network Analysis Techniques
Papers in
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- Distributed and Parallel Computing Systems 9
- Peer-to-Peer Network Technologies 6
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- Reinforcement Learning in Robotics 11
- Evolutionary Algorithms and Applications 10
- Logic, programming, and type systems 7
- Co-authors
- Pedro Ribeiro (23 shared papers)José Paulo Leal (1 shared paper)Anders Lyhne Christensen (13 shared papers)Luís Lopes (15 shared papers)Vı́tor Santos Costa (8 shared papers)David Aparício (7 shared papers)Sancho Oliveira (8 shared papers)Luís Correia (8 shared papers)
- Journals
- Data Mining and Knowledge Discovery (4 papers)Software Practice and Experience (4 papers)Evolutionary Computation (2 papers)Bioinformatics (1 paper)Machine Learning (1 paper)
- Partner nations
- PortugalUnited StatesBrazil
In The Last Decade
Fernando Silva
68 papers receiving 839 citations
Peers
Comparison fields: 5 of 93
- Statistical and Nonlinear Physics 206
- Computer Science Applications 89
- Artificial Intelligence 349
- Software 40
- Computer Networks and Communications 205
Countries citing papers authored by Fernando Silva
This map shows the geographic impact of Fernando Silva'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 Fernando Silva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Silva more than expected).
Fields of papers citing papers by Fernando Silva
This network shows the impact of papers produced by Fernando Silva. 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 Fernando Silva. The network helps show where Fernando Silva may publish in the future.
Co-authors
The 25 scholars most cited alongside Fernando Silva, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2003 | 87 | |
| 2 | 2016 | 66 | |
| 3 | 2010 | 64 | |
| 4 | 2014 | 59 | |
| 5 | 2021 | 54 | |
| 6 | 2009 | 38 | |
| 7 | 2013 | 32 | |
| 8 | 2012 | 25 | |
| 9 | 2014 | 24 | |
| 10 | 2012 | 23 | |
| 11 | 2011 | 22 | |
| 12 | 2015 | 20 | |
| 13 | 2014 | 19 | |
| 14 | 2015 | 19 | |
| 15 | 2010 | 18 | |
| 16 | 2017 | 18 | |
| 17 | 2017 | 16 | |
| 18 | 2019 | 16 | |
| 19 | 2005 | 16 | |
| 20 | 2014 | 15 |
About Fernando Silva
Fernando Silva is a scholar working on Computer Networks and Communications, Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology and Information Systems, having authored 74 papers that have together received 870 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (19 papers), Reinforcement Learning in Robotics (11 papers), Evolutionary Algorithms and Applications (10 papers), Distributed and Parallel Computing Systems (9 papers), Bioinformatics and Genomic Networks (9 papers), Parallel Computing and Optimization Techniques (8 papers), Logic, programming, and type systems (7 papers) and Peer-to-Peer Network Technologies (6 papers). The work is most often cited by research in Statistical and Nonlinear Physics (206 citations), Computer Science Applications (89 citations), Artificial Intelligence (349 citations), Software (40 citations) and Computer Networks and Communications (205 citations). Fernando Silva has collaborated with scholars based in Portugal, United States and Brazil. Frequent co-authors include Pedro Ribeiro, José Paulo Leal, Anders Lyhne Christensen, Luís Lopes, Vı́tor Santos Costa, David Aparício, Sancho Oliveira, Luís Correia, Miguel Duarte and Inês Dutra. Their work appears in journals such as Data Mining and Knowledge Discovery, Software Practice and Experience, Evolutionary Computation, Bioinformatics 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.