Manuela Aguiar

489 citations
30 papers · 331 · h-index 10

Impact in

Papers in

Manuela Aguiar

28 papers receiving 313 citations

Peers

Manuela Aguiar
Comparison fields: 5 of 47
  • Statistical and Nonlinear Physics 141
  • Computer Networks and Communications 244
  • Cognitive Neuroscience 79
  • Geometry and Topology 34
  • Mathematical Physics 33
Replace Ana Paula S. Dias with:
Ana Paula S. Dias Portugal
Georgi S. Medvedev United States
Nikos E. Kouvaris Greece
Ivan Bonamassa Israel
Isabel S. Labouriau Portugal
Grégory Faye France
Delphine Salort France
Ana P. Millán Spain
H. Wang Netherlands
Manuela Aguiar relative to Ana Paula S. Dias Portugal Ana Paula S. Dias's profile →
Citations per field
00.5×1.6×
Ana Paula S. Dias · 1×
Citations per year

Countries citing papers authored by Manuela Aguiar

Since Specialization
Citations

This map shows the geographic impact of Manuela Aguiar'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 Manuela Aguiar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manuela Aguiar more than expected).

Fields of papers citing papers by Manuela Aguiar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Manuela Aguiar. 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 Manuela Aguiar. The network helps show where Manuela Aguiar may publish in the future.

Co-authors

The 12 scholars most cited alongside Manuela Aguiar, 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 Manuela Aguiar Line = papers co-authored together Manuela Aguiar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201070
2 200439
3 200838
4 201426
5 200922
6 201116
7 201815
8 200614
9 201014
10 201713
11 20069
12 20187
13 20236
14
Vector fields with heteroclinic networks
20026
15 20115
16 20125
17 20154
18 20184
19 20213
20 20163

About Manuela Aguiar

Manuela Aguiar is a scholar working on Computer Networks and Communications, Molecular Biology, Cognitive Neuroscience, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 30 papers that have together received 331 indexed citations. Recurring topics across this work include Nonlinear Dynamics and Pattern Formation (23 papers), Gene Regulatory Network Analysis (15 papers), Neural dynamics and brain function (13 papers), Cellular Automata and Applications (5 papers), Neural Networks Stability and Synchronization (5 papers), Photoreceptor and optogenetics research (4 papers), Quantum chaos and dynamical systems (3 papers) and Complex Network Analysis Techniques (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (141 citations), Computer Networks and Communications (244 citations), Cognitive Neuroscience (79 citations), Geometry and Topology (34 citations) and Mathematical Physics (33 citations). Manuela Aguiar has collaborated with scholars based in Portugal, Germany and United Kingdom. Frequent co-authors include Ana Paula S. Dias, Isabel S. Labouriau, Sofia B. S. D. Castro, Martha S. Field, Peter Ashwin, Martin Golubitsky, Maria C. A. Leite, Flora Ferreira, Michael Field and Christian Bick. Their work appears in journals such as Nonlinearity, Physica D Nonlinear Phenomena, Journal of Nonlinear Science, Chaos An Interdisciplinary Journal of Nonlinear Science and Dynamical Systems.

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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