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.
Rapid assessment of disaster damage using social media activity
2016447 citationsYury Kryvasheyeu, Haohui Chen et al.profile →
Toward understanding the impact of artificial intelligence on labor
2019349 citationsMorgan R. Frank, Manuel Cebrián et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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Countries citing papers authored by Manuel Cebrián
Since
Specialization
Citations
This map shows the geographic impact of Manuel Cebrián'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 Manuel Cebrián with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manuel Cebrián more than expected).
This network shows the impact of papers produced by Manuel Cebrián. 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 Manuel Cebrián. The network helps show where Manuel Cebrián may publish in the future.
Co-authorship network of co-authors of Manuel Cebrián
This figure shows the co-authorship network connecting the top 25 collaborators of Manuel Cebrián.
A scholar is included among the top collaborators of Manuel Cebrián 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 Manuel Cebrián. Manuel Cebrián is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Morales, Aythami, et al.. (2019). Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics. MPG.PuRe (Max Planck Society). 146–152.11 indexed citations
6.
Roy, Kamol Chandra, Samiul Hasan, Arif Mohaimin Sadri, & Manuel Cebrián. (2018). Understanding the Effectiveness of Social Media Based Crisis Communication During Hurricane Sandy. Transportation Research Board 97th Annual MeetingTransportation Research Board.2 indexed citations
7.
Baylis, Patrick, Nick Obradovich, Yury Kryvasheyeu, et al.. (2018). Weather impacts expressed sentiment. PLoS ONE. 13(4). e0195750–e0195750.108 indexed citations
8.
Crandall, Jacob W., Sherief Abdallah, Jean‐François Bonnefon, et al.. (2018). Cooperating with machines. Nature Communications. 9(1). 233–233.130 indexed citations
Naroditskiy, Victor, Iyad Rahwan, Manuel Cebrián, & Nicholas R. Jennings. (2012). Verification in Referral-Based Crowdsourcing. PLoS ONE. 7(10). e45924–e45924.26 indexed citations
17.
Cebrián, Manuel, Iván Dotú, Pascal Van Hentenryck, & Peter Clote. (2008). Protein structure prediction on the face centered cubic lattice by local search. National Conference on Artificial Intelligence. 241–246.23 indexed citations
18.
Alfonseca, Manuel, Manuel Cebrián, & Alfonso Ortega. (2006). Testing genetic algorithm recombination strategies and the normalized compression distance for computer-generated music. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 53–58.
19.
Alfonseca, Manuel, Manuel Cebrián, & Alfonso Ortega. (2005). Evolving computer-generated music by means of the normalized compression distance. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 343–348.5 indexed citations
20.
Val, Álvaro del, et al.. (2003). Channeling constraints and value ordering in the quasigroup completion problem. International Joint Conference on Artificial Intelligence. 1372–1373.1 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.