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.
A Survey on Network Codes for Distributed Storage
2011369 citationsKannan Ramchandran, Changho Suh et al.profile →
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 Changho Suh'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 Changho Suh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Changho Suh more than expected).
This network shows the impact of papers produced by Changho Suh. 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 Changho Suh. The network helps show where Changho Suh may publish in the future.
Co-authorship network of co-authors of Changho Suh
This figure shows the co-authorship network connecting the top 25 collaborators of Changho Suh.
A scholar is included among the top collaborators of Changho Suh 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 Changho Suh. Changho Suh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Suh, Changho, et al.. (2020). A Fair Classifier Using Kernel Density Estimation. Neural Information Processing Systems. 33. 15088–15099.10 indexed citations
Lee, Kangwook, et al.. (2018). SGD on Random Mixtures: Private Machine Learning under Data Breach Threats. International Conference on Learning Representations.1 indexed citations
7.
Ahn, Kwangjun, et al.. (2018). Binary Rating Estimation with Graph Side Information. Neural Information Processing Systems. 31. 4272–4283.15 indexed citations
Suh, Changho, et al.. (2017). Optimal Sample Complexity of M-wise Data for Top-K Ranking. neural information processing systems. 30. 1687–1697.7 indexed citations
10.
Lee, Kangwook, et al.. (2017). Crash To Not Crash: Playing Video Games To Predict Vehicle Collisions. International Conference on Machine Learning.4 indexed citations
11.
Lee, Kangwook, et al.. (2016). Learning analytics: Collaborative filtering or regression with experts?. Neural Information Processing Systems.
12.
Chen, Yuxin, Govinda M. Kamath, Changho Suh, & David Tse. (2016). Community recovery in graphs with locality. International Conference on Machine Learning. 689–698.6 indexed citations
Suh, Changho, et al.. (2004). Optimal power allocation for type II H-ARQ under frame error rate constraint.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.