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.
Bibliographic coupling between scientific papers
19631.9k citationsM. M. KesslerAmerican Documentationprofile →
This map shows the geographic impact of M. M. Kessler'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 M. M. Kessler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. M. Kessler more than expected).
This network shows the impact of papers produced by M. M. Kessler. 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 M. M. Kessler. The network helps show where M. M. Kessler may publish in the future.
M. M. Kessler is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability and Management Information Systems, having authored 6 papers that have together received 2.1k indexed citations. Recurring topics across this work include scientometrics and bibliometrics research (2 papers), Advanced Data Processing Techniques (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Statistics, Probability and Uncertainty (422 citations), Business and International Management (56 citations) and Management of Technology and Innovation (181 citations). M. M. Kessler has collaborated with scholars based in United States. Their work appears in journals such as Physics Today, IEEE Transactions on Information Theory and American Documentation.
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.