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.
An efficient k-means clustering algorithm: analysis and implementation
20023.9k citationsTapas Kanungo, David M. Mount et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
A visibility matching tone reproduction operator for high dynamic range scenes
1997535 citationsGregory Ward Larson, Holly Rushmeier et al.IEEE Transactions on Visualization and Computer Graphicsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Christine Piatko
Since
Specialization
Citations
This map shows the geographic impact of Christine Piatko'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 Christine Piatko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christine Piatko more than expected).
Fields of papers citing papers by Christine Piatko
This network shows the impact of papers produced by Christine Piatko. 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 Christine Piatko. The network helps show where Christine Piatko may publish in the future.
Co-authorship network of co-authors of Christine Piatko
This figure shows the co-authorship network connecting the top 25 collaborators of Christine Piatko.
A scholar is included among the top collaborators of Christine Piatko 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 Christine Piatko. Christine Piatko is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
McNamee, Paul, James Mayfield, & Christine Piatko. (2011). Processing Named Entities in Text.2 indexed citations
4.
Sayeed, Asad, Tamer Elsayed, Nikesh Garera, et al.. (2009). Arabic Cross-Document Coreference Resolution. Meeting of the Association for Computational Linguistics. 357–360.
McNamee, Paul, James Mayfield, & Christine Piatko. (2002). Haircut: a system for multilingual text retrieval in java. Journal of computing sciences in colleges. 17(3). 8–22.3 indexed citations
11.
Kanungo, Tapas, David M. Mount, Nathan S. Netanyahu, et al.. (2002). An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 24(7). 881–892.3878 indexed citations breakdown →
Mayfield, James, Paul McNamee, Cash Costello, Christine Piatko, & Amit Banerjee. (2001). JHU/APL at TREC 2001: Experiments in Filtering and in Arabic, Video, and Web Retrieval. Text REtrieval Conference.27 indexed citations
Kanungo, Tapas, David M. Mount, Nathan S. Netanyahu, et al.. (1999). Computing nearest neighbors for moving points and applications to clustering. Symposium on Discrete Algorithms. 931–932.16 indexed citations
18.
Mayfield, James, Paul McNamee, & Christine Piatko. (1999). The JHU/APL HAIRCUT System at TREC-8.. Text REtrieval Conference.19 indexed citations
19.
Larson, Gregory Ward, Holly Rushmeier, & Christine Piatko. (1997). A visibility matching tone reproduction operator for high dynamic range scenes. IEEE Transactions on Visualization and Computer Graphics. 3(4). 291–306.535 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.