Kun Gai

About

Kun Gai is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kun Gai has authored 47 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Information Systems, 23 papers in Artificial Intelligence and 23 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kun Gai’s work include Recommender Systems and Techniques (26 papers), Advanced Bandit Algorithms Research (12 papers) and Advanced Graph Neural Networks (7 papers). Kun Gai is often cited by papers focused on Recommender Systems and Techniques (26 papers), Advanced Bandit Algorithms Research (12 papers) and Advanced Graph Neural Networks (7 papers). Kun Gai collaborates with scholars based in China, United States and Hong Kong. Kun Gai's co-authors include Changshui Zhang, Guorui Zhou, Xiaoqiang Zhu and Yang Song and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence and Neurocomputing.

In The Last Decade

Fields of papers citing papers by Kun Gai

Since Specialization

EngineeringComputer SciencePhysics and AstronomyMathematicsEarth and Planetary SciencesEnergyEnvironmental ScienceMaterials ScienceChemical EngineeringChemistryAgricultural and Biological SciencesVeterinaryDecision SciencesArts and HumanitiesBusiness, Management and AccountingSocial SciencesPsychologyEconomics, Econometrics and FinanceHealth ProfessionsDentistryMedicineBiochemistry, Genetics and Molecular BiologyNeuroscienceNursingImmunology and MicrobiologyPharmacology, Toxicology and Pharmaceutics

This network shows the specialization of papers citing the papers produced by Kun Gai. Nodes represent fields, and links connect fields that are likely to share authors. The network helps show where Kun Gai may publish in the future.

Countries citing papers authored by Kun Gai

Since Specialization

Citations

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

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