Frontiers in Big Data

510 papers and 4.0k indexed citations

About

The 510 papers published in Frontiers in Big Data in the last decades have received a total of 4.0k indexed citations. Papers published in Frontiers in Big Data usually cover Artificial Intelligence (186 papers), Information Systems (84 papers) and Sociology and Political Science (59 papers) specifically the topics of Complex Network Analysis Techniques (35 papers), Scientific Computing and Data Management (25 papers) and Recommender Systems and Techniques (24 papers). The most active scholars publishing in Frontiers in Big Data are Alexandra Olteanu, Carlos Castillo, Emre Kıcıman, Fernando Díaz, Kristian Kersting, Lars Lau Rakêt, Willem J. M. I. Verbeke, Felix Bießmann, Xia Hu and Mónica Cano Abadía.

In The Last Decade

Frontiers in Big Data

420 papers receiving 3.9k citations

Fields of papers published in Frontiers in Big Data

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Frontiers in 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 Frontiers in Big Data.

Countries where authors publish in Frontiers in Big Data

Since Specialization
Citations

This map shows the geographic impact of research published in Frontiers in 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 Frontiers in 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 Frontiers in 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