Hanjun Dai

About

Hanjun Dai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Hanjun Dai has authored 28 papers receiving a total of 985 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Hanjun Dai’s work include Topic Modeling (6 papers), Advanced Graph Neural Networks (5 papers) and Bayesian Methods and Mixture Models (4 papers). Hanjun Dai is often cited by papers focused on Topic Modeling (6 papers), Advanced Graph Neural Networks (5 papers) and Bayesian Methods and Mixture Models (4 papers). Hanjun Dai collaborates with scholars based in United States, Canada and China. Hanjun Dai's co-authors include Le Song, Yuyu Zhang, Zornitsa Kozareva, Alexander J. Smola, Ahmet Cecen, Yuksel C. Yabansu, Surya R. Kalidindi, Le Song, Tie‐Yan Liu and 将尚渡辺 and has published in prestigious journals such as Bioinformatics, Chemistry of Materials and Acta Materialia.

In The Last Decade

Co-authorship network of co-authors of Hanjun Dai i

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Fields of papers citing papers by Hanjun Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hanjun Dai. 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 Hanjun Dai. The network helps show where Hanjun Dai may publish in the future.

Countries citing papers authored by Hanjun Dai

Since Specialization

Citations

This map shows the geographic impact of Hanjun Dai'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 Hanjun Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hanjun Dai 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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