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.
Integrating constraints and metric learning in semi-supervised clustering
2004525 citationsMikhail Bilenko, Sugato Basu et al.profile →
A probabilistic framework for semi-supervised clustering
2004512 citationsSugato Basu, Mikhail Bilenko et al.profile →
This map shows the geographic impact of Sugato Basu'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 Sugato Basu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sugato Basu more than expected).
This network shows the impact of papers produced by Sugato Basu. 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 Sugato Basu. The network helps show where Sugato Basu may publish in the future.
Co-authorship network of co-authors of Sugato Basu
This figure shows the co-authorship network connecting the top 25 collaborators of Sugato Basu.
A scholar is included among the top collaborators of Sugato Basu 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 Sugato Basu. Sugato Basu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhu, Wanrong, Yuankai Qi, Pradyumna Narayana, et al.. (2022). Diagnosing Vision-and-Language Navigation: What Really Matters. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5981–5993.27 indexed citations
Wagstaff, Kiri L., Sugato Basu, & Ian Davidson. (2006). When is constrained clustering beneficial, and why?. National Conference on Artificial Intelligence.36 indexed citations
13.
Kulis, Brian, Sugato Basu, Inderjit S. Dhillon, & Raymond J. Mooney. (2005). Semi-supervised graph clustering. 457–464.106 indexed citations
14.
Banerjee, Arindam, Chase Krumpelman, Joydeep Ghosh, Sugato Basu, & Raymond J. Mooney. (2005). Model-based overlapping clustering. 532–537.128 indexed citations
15.
Basu, Sugato. (2004). Semi-supervised clustering with limited background knowledge. National Conference on Artificial Intelligence. 979–980.5 indexed citations
16.
Basu, Sugato, Mikhail Bilenko, & Raymond J. Mooney. (2004). A probabilistic framework for semi-supervised clustering. 59–68.512 indexed citations breakdown →
Basu, Sugato, Mikhail Bilenko, & Raymond J. Mooney. (2003). Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering.54 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.