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.
Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix
2009341 citationsZhouchen Lin, Arvind Ganesh et al.IDEALS (University of Illinois Urbana-Champaign)profile →
This map shows the geographic impact of Leqin Wu'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 Leqin Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leqin Wu more than expected).
This network shows the impact of papers produced by Leqin Wu. 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 Leqin Wu. The network helps show where Leqin Wu may publish in the future.
Co-authorship network of co-authors of Leqin Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Leqin Wu.
A scholar is included among the top collaborators of Leqin Wu 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 Leqin Wu. Leqin Wu 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.