Journal of Automated Reasoning

1.1k papers and 13.4k indexed citations i.

About

The 1.1k papers published in Journal of Automated Reasoning in the last decades have received a total of 13.4k indexed citations. Papers published in Journal of Automated Reasoning usually cover Artificial Intelligence (939 papers), Computational Theory and Mathematics (670 papers) and Computer Networks and Communications (173 papers) specifically the topics of Logic, programming, and type systems (698 papers), Logic, Reasoning, and Knowledge (467 papers) and Formal Methods in Verification (422 papers). The most active scholars publishing in Journal of Automated Reasoning are Geoff Sutcliffe, Lawrence C. Paulson, Xavier Leroy, Wen-tsün Wu, William McCune, André Platzer, M.E. Stickel, Franz Baader, Mark E. Stickel and Larry Wos.

In The Last Decade

Fields of papers published in Journal of Automated Reasoning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Journal of Automated Reasoning. 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 Journal of Automated Reasoning.

Countries where authors publish in Journal of Automated Reasoning

Since Specialization
Citations

This map shows the geographic impact of research published in Journal of Automated Reasoning. 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 Journal of Automated Reasoning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Journal of Automated Reasoning 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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