Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Social big data: Recent achievements and new challenges
2015536 citationsGema Bello-Orgaz, Jason J. Jung et al.Information Fusionprofile →
Bio-inspired computation: Where we stand and what's next
2019438 citationsJavier Del Ser, David Camacho et al.Swarm and Evolutionary Computationprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of David Camacho'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 David Camacho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Camacho more than expected).
This network shows the impact of papers produced by David Camacho. 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 David Camacho. The network helps show where David Camacho may publish in the future.
Co-authorship network of co-authors of David Camacho
This figure shows the co-authorship network connecting the top 25 collaborators of David Camacho.
A scholar is included among the top collaborators of David Camacho 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 David Camacho. David Camacho is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Camacho, David, Juan Gómez‐Romero, & Jason J. Jung. (2024). Special issue on infodemics. Journal of Ambient Intelligence and Humanized Computing. 15(3). 1975–1980.
Huertas‐Tato, Javier, et al.. (2021). CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic Classification.. CLEF (Working Notes). 520–530.4 indexed citations
9.
Camacho, David, et al.. (2021). El verd escolar: dret o privilegi? : El cas de Barcelona.1 indexed citations
Menéndez, Héctor D., Fernando E. B. Otero, & David Camacho. (2015). SACOC: A spectral-based ACO clustering algorithm. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas).1 indexed citations
15.
González-Pardo, Antonio, et al.. (2015). An empirical study on collective intelligence algorithms for video games problem-solving. BIRD (Basque Center for Applied Mathematics). 34(1). 233–253.8 indexed citations
16.
Bello-Orgaz, Gema, et al.. (2014). Combining social-based data mining techniques to extract collective trends from twitter. Malaysian Journal of Computer Science. 27(2). 95–111.18 indexed citations
Rico, Mariano, David Camacho, & Óscar Corcho. (2009). VPOET Templates to Handle the Presentation of Semantic Data Sources in Wikis. Biblos-e Archivo (Universidad Autónoma de Madrid).1 indexed citations
19.
Camacho, David, Ricardo Aler, Daniel Borrajo, & José M. Molina. (2005). A multi-agent architecture for intelligent gathering systems. AI Communications. 18(1). 15–32.17 indexed citations
20.
Camacho, David. (2003). Coordination of planning agents to solve problems in the Web: Thesis. AI Communications. 16(4). 309–311.1 indexed citations
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.