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.
Countries citing papers authored by Fernanda Viégas
Since
Specialization
Citations
This map shows the geographic impact of Fernanda Viégas'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 Fernanda Viégas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernanda Viégas more than expected).
This network shows the impact of papers produced by Fernanda Viégas. 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 Fernanda Viégas. The network helps show where Fernanda Viégas may publish in the future.
Co-authorship network of co-authors of Fernanda Viégas
This figure shows the co-authorship network connecting the top 25 collaborators of Fernanda Viégas.
A scholar is included among the top collaborators of Fernanda Viégas 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 Fernanda Viégas. Fernanda Viégas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Smilkov, Daniel, Nikhil Thorat, Ann Yuan, et al.. (2019). TensorFlow.js: Machine Learning for the Web and Beyond. arXiv (Cornell University). 1. 309–321.5 indexed citations
6.
Reif, Emily, Ann Yuan, Martin Wattenberg, et al.. (2019). Visualizing and Measuring the Geometry of BERT. Neural Information Processing Systems. 32. 8592–8600.70 indexed citations
DeVries, Phoebe M. R., Fernanda Viégas, Martin Wattenberg, & Brendan J. Meade. (2018). Deep learning of aftershock patterns following large earthquakes. Nature. 560(7720). 632–634.223 indexed citations breakdown →
Donath, Judith, et al.. (2010). Data Portraits. Leonardo. 43(4). 375–383.7 indexed citations
11.
Ham, Frank van, Martin Wattenberg, & Fernanda Viégas. (2009). Mapping Text with Phrase Nets. IEEE Transactions on Visualization and Computer Graphics. 15(6). 1169–1176.107 indexed citations
12.
Viégas, Fernanda, Martin Wattenberg, & J. FEINBERG. (2009). Participatory Visualization with Wordle. IEEE Transactions on Visualization and Computer Graphics. 15(6). 1137–1144.246 indexed citations
13.
Wattenberg, Martin & Fernanda Viégas. (2008). The Word Tree, an Interactive Visual Concordance. IEEE Transactions on Visualization and Computer Graphics. 14(6). 1221–1228.220 indexed citations
14.
Viégas, Fernanda, et al.. (2007). ManyEyes: a Site for Visualization at Internet Scale. IEEE Transactions on Visualization and Computer Graphics. 13(6). 1121–1128.484 indexed citations breakdown →
Viégas, Fernanda, Martin Wattenberg, & Kushal Dave. (2004). Studying cooperation and conflict between authors with history flow visualizations. 575–582.528 indexed citations breakdown →
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.