Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.
Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.
Countries citing papers authored by Raphael Labaca-Castro
Since Specialization
Citations
This map shows the geographic impact of Raphael Labaca-Castro'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 Raphael Labaca-Castro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raphael Labaca-Castro more than expected).
Fields of papers citing papers by Raphael Labaca-Castro
This network shows the impact of papers produced by Raphael Labaca-Castro. 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 Raphael Labaca-Castro. The network helps show where Raphael Labaca-Castro may publish in the future.
Co-authors
The 2 scholars most cited alongside Raphael Labaca-Castro, 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 Raphael Labaca-CastroLine = papers co-authored togetherRaphael Labaca-Castro links everyone, so they are left out of the graph.
Raphael Labaca-Castro is a scholar working on Signal Processing, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition and Nuclear Energy and Engineering, having authored 5 papers that have together received 4.5k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Advanced Malware Detection Techniques (2 papers), Generative Adversarial Networks and Image Synthesis (1 paper) and Network Security and Intrusion Detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.8k citations), Health Informatics (90 citations), Artificial Intelligence (1.1k citations), Signal Processing (142 citations) and Media Technology (125 citations). Raphael Labaca-Castro has collaborated with scholars based in Germany and Italy. Frequent co-authors include Gabi Dreo Rodosek and Battista Biggio. Their work appears in journals such as Lecture notes in computer science, Zenodo (CERN European Organization for Nuclear Research), ACM SIGSAC Conference on Computer and Communications Security (CCS) and Springer eBooks.
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.