Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Blue
2002592 citationsMurray Campbell, Feng-hsiung Hsu et al.Artificial Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Murray Campbell
Since
Specialization
Citations
This map shows the geographic impact of Murray Campbell'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 Murray Campbell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Murray Campbell more than expected).
This network shows the impact of papers produced by Murray Campbell. 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 Murray Campbell. The network helps show where Murray Campbell may publish in the future.
Co-authorship network of co-authors of Murray Campbell
This figure shows the co-authorship network connecting the top 25 collaborators of Murray Campbell.
A scholar is included among the top collaborators of Murray Campbell based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Murray Campbell. Murray Campbell is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Guo, Xiaoxiao, Tim Klinger, Joseph P. Bigus, et al.. (2017). Learning to Query, Reason, and Answer Questions On Ambiguous Texts. International Conference on Learning Representations.7 indexed citations
7.
Wang, Shuohang, Mo Yu, Jing Jiang, et al.. (2017). Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering. Singapore Management University Institutional Knowledge (InK) (Singapore Management University). 1.49 indexed citations
8.
Machado, Marlos C., et al.. (2017). Eigenoption Discovery through the Deep Successor Representation. arXiv (Cornell University).2 indexed citations
9.
Woolley, A. R., et al.. (2015). Proceedings of the Third Vienna Talk on Music Acoustics.2 indexed citations
Kemp, Jonathan, et al.. (2010). Analysis of Transients for Brass Instruments Under Playing Conditions Using Multiple Microphones. HAL (Le Centre pour la Communication Scientifique Directe).1 indexed citations
Campbell, Murray, et al.. (2006). IBM Research TRECVID-2006 Video Retrieval System. TRECVID.77 indexed citations
15.
Walstijn, Maarten van, et al.. (2005). Wideband measurement of the acoustic impedance of tubular objects. Open Research Online (The Open University).19 indexed citations
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.