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.
Learning From Noisy Labels With Deep Neural Networks: A Survey
2022568 citationsHwanjun Song, Minseok Kim et al.IEEE Transactions on Neural Networks and Learning Systemsprofile →
This map shows the geographic impact of Yooju Shin'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 Yooju Shin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yooju Shin more than expected).
This network shows the impact of papers produced by Yooju Shin. 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 Yooju Shin. The network helps show where Yooju Shin may publish in the future.
Co-authorship network of co-authors of Yooju Shin
This figure shows the co-authorship network connecting the top 25 collaborators of Yooju Shin.
A scholar is included among the top collaborators of Yooju Shin 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 Yooju Shin. Yooju Shin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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.