Fernando Silva

1.9k citations
74 papers · 870 indexed · h-index 17

Impact in

Papers in

Fernando Silva

68 papers receiving 839 citations

Peers

Fernando Silva
Comparison fields: 5 of 93
  • Statistical and Nonlinear Physics 206
  • Computer Science Applications 89
  • Artificial Intelligence 349
  • Software 40
  • Computer Networks and Communications 205
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Thomas E. Potok United States
Zheng Xie China
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Citations per field
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Citations per year

Countries citing papers authored by Fernando Silva

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Fernando Silva Line = papers co-authored together Fernando Silva links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20231
3 20195
4 201813
5 201611
6 20153
7 20151
8 201419
9 20131
10 201212
11 201225
12 201018
13 20092
14 200812
15 20005
16 199813
17
The SBA: exploiting orthogonality in AND-OR parallel systems
19974
18 19970
19
Aurora, Andorra-I and Friends on the Sun.
19941
20
Initial Performance of Dorpp: an Or-Parallel Prolog System for a Distributed Shared Memory Architecture.
19931

About Fernando Silva

Fernando Silva is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications, Hardware and Architecture, Artificial Intelligence and Signal Processing, 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, Sancho Oliveira, David Aparício, Vı́tor Santos Costa, Luís Lopes, 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.

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