IEEE Transactions on Big Data

About

The 809 papers published in IEEE Transactions on Big Data in the last decades have received a total of 11.9k indexed citations. Papers published in IEEE Transactions on Big Data usually cover Artificial Intelligence (418 papers), Information Systems (196 papers) and Computer Vision and Pattern Recognition (186 papers) specifically the topics of Privacy-Preserving Technologies in Data (112 papers), Advanced Graph Neural Networks (91 papers) and Complex Network Analysis Techniques (78 papers). The most active scholars publishing in IEEE Transactions on Big Data are Yu Zheng, Xingquan Zhu, Marta C. González, Shan Jiang, Joseph Ferreira, Jie Yin, Daokun Zhang, Chengqi Zhang, Laurence T. Yang and Feng Xia.

In The Last Decade

IEEE Transactions on Big Data

691 papers receiving 11.4k citations

Fields of papers published in IEEE Transactions on Big Data

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in IEEE Transactions on Big Data. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in IEEE Transactions on Big Data.

Countries where authors publish in IEEE Transactions on Big Data

Since Specialization
Citations

This map shows the geographic impact of research published in IEEE Transactions on Big Data. 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 papers published in IEEE Transactions on Big Data with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites IEEE Transactions on Big Data 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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2026