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.
Countries citing papers authored by Jayant Madhavan
Since
Specialization
Citations
This map shows the geographic impact of Jayant Madhavan'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 Jayant Madhavan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jayant Madhavan more than expected).
This network shows the impact of papers produced by Jayant Madhavan. 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 Jayant Madhavan. The network helps show where Jayant Madhavan may publish in the future.
Co-authorship network of co-authors of Jayant Madhavan
This figure shows the co-authorship network connecting the top 25 collaborators of Jayant Madhavan.
A scholar is included among the top collaborators of Jayant Madhavan 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 Jayant Madhavan. Jayant Madhavan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Balakrishnan, S., Alon Halevy, Boulos Harb, et al.. (2015). Applying WebTables in Practice. Conference on Innovative Data Systems Research.36 indexed citations
2.
Madhavan, Jayant, S. Balakrishnan, Héctor González, et al.. (2012). Big Data Storytelling Through Interactive Maps. IEEE Data(base) Engineering Bulletin. 35. 46–54.9 indexed citations
Bernstein, Philip A., Jayant Madhavan, & Erhard Rahm. (2011). Generic schema matching, ten years later. Proceedings of the VLDB Endowment. 4(11). 695–701.146 indexed citations
5.
González, Héctor, et al.. (2010). Socialising Data with Google Fusion Tables.. IEEE Data(base) Engineering Bulletin. 33. 25–32.26 indexed citations
Madhavan, Jayant, Shawn R. Jeffery, Shirley Cohen, et al.. (2007). Web-scale Data Integration: You can only afford to Pay As You Go. Conference on Innovative Data Systems Research. 342–350.199 indexed citations
12.
Madhavan, Jayant, Alon Halevy, Shirley Cohen, et al.. (2006). Structured Data Meets the Web: A Few Observations. IEEE Data(base) Engineering Bulletin. 29. 19–26.25 indexed citations
Halevy, Alon, Jayant Madhavan, & Philip A. Bernstein. (2003). Discovering Structure in a Corpus of Schemas.. IEEE Data(base) Engineering Bulletin. 26. 26–33.4 indexed citations
17.
Halevy, Alon & Jayant Madhavan. (2003). Corpus-based knowledge representation. International Joint Conference on Artificial Intelligence. 1567–1572.16 indexed citations
18.
Domingos, Pedro, Yeuhi Abe, Corin R. Anderson, et al.. (2003). Research on Statistical Relational Learning at the University of Washington.1 indexed citations
Madhavan, Jayant, Philip A. Bernstein, & Erhard Rahm. (2001). Generic Schema Matching with Cupid. Qucosa (Saxon State and University Library Dresden). 49–58.704 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.