In-Su Kang

27 papers and 198 indexed citations i.

About

In-Su Kang is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, In-Su Kang has authored 27 papers receiving a total of 198 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 11 papers in Information Systems and 6 papers in Management Science and Operations Research. Recurrent topics in In-Su Kang’s work include Topic Modeling (15 papers), Natural Language Processing Techniques (12 papers) and Web Data Mining and Analysis (7 papers). In-Su Kang is often cited by papers focused on Topic Modeling (15 papers), Natural Language Processing Techniques (12 papers) and Web Data Mining and Analysis (7 papers). In-Su Kang collaborates with scholars based in South Korea, United Kingdom and United States. In-Su Kang's co-authors include Jong-Hyeok Lee, Seung‐Hoon Na, Hanmin Jung, Seungwoo Lee, Won-Kyung Sung, Jun-Gi Kim, Seung‐Wan Song, Hyunsoo Kim, Bo Hou and Deok‐kee Kim and has published in prestigious journals such as Sensors and Actuators B Chemical, Information Processing & Management and Materials Research Express.

In The Last Decade

Co-authorship network of co-authors of In-Su Kang i

Fields of papers citing papers by In-Su Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by In-Su Kang. 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 In-Su Kang. The network helps show where In-Su Kang may publish in the future.

Countries citing papers authored by In-Su Kang

Since Specialization
Citations

This map shows the geographic impact of In-Su Kang'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 In-Su Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites In-Su Kang more than expected).

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.

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2025