Alicia d’Anjou

90 total papers · 1.1k total citations
49 papers, 756 citations indexed

About

Alicia d’Anjou is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Computer Networks and Communications. According to data from OpenAlex, Alicia d’Anjou has authored 49 papers receiving a total of 756 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 17 papers in Statistical and Nonlinear Physics and 13 papers in Computer Networks and Communications. Recurrent topics in Alicia d’Anjou's work include Nonlinear Dynamics and Pattern Formation (12 papers), Neural dynamics and brain function (9 papers) and Neural Networks and Applications (9 papers). Alicia d’Anjou is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (12 papers), Neural dynamics and brain function (9 papers) and Neural Networks and Applications (9 papers). Alicia d’Anjou collaborates with scholars based in Spain, France and United Kingdom. Alicia d’Anjou's co-authors include Francisco Torrealdea, Manuel Graña, Abdelmalik Moujahid, C. Sarasola, F. Sanz, Nieves Vélez de Mendizábal, Carmen Hernández, Ramón Moreno, Raúl Orduna-Urrutia and José M. Asúa and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Information Sciences and Journal of Non-Crystalline Solids.

In The Last Decade

Alicia d’Anjou

48 papers receiving 706 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Alicia d’Anjou	399	318	260	127	126	49	756
Zhigang Zhu	399 1.0×	387 1.2×	162 0.6×	135 1.1×	251 2.0×	29	622
Yunzhen Zhang	571 1.4×	337 1.1×	299 1.1×	42 0.3×	411 3.3×	34	776
Stavros G. Stavrinides	275 0.7×	102 0.3×	145 0.6×	157 1.2×	309 2.5×	91	629
Euripidis Glavas	54 0.1×	215 0.7×	70 0.3×	25 0.2×	198 1.6×	66	813
Shenquan Liu	378 0.9×	323 1.0×	264 1.0×	91 0.7×	67 0.5×	75	644
Chenggui Yao	487 1.2×	266 0.8×	400 1.5×	58 0.5×	81 0.6×	53	792
Bei Chen	493 1.2×	301 0.9×	230 0.9×	49 0.4×	410 3.3×	34	757
Xiaoli Yang	524 1.3×	283 0.9×	374 1.4×	89 0.7×	63 0.5×	76	890
Douglas G. Kelly	120 0.3×	210 0.7×	177 0.7×	49 0.4×	89 0.7×	22	857
Mathias Bode	244 0.6×	78 0.2×	400 1.5×	19 0.1×	165 1.3×	31	713

Countries citing papers authored by Alicia d’Anjou

Since Specialization

Citations

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

Fields of papers citing papers by Alicia d’Anjou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alicia d’Anjou. 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 Alicia d’Anjou. The network helps show where Alicia d’Anjou may publish in the future.

Co-authorship network of co-authors of Alicia d’Anjou

This figure shows the co-authorship network connecting the top 25 collaborators of Alicia d’Anjou. A scholar is included among the top collaborators of Alicia d’Anjou 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 Alicia d’Anjou. Alicia d’Anjou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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