Masashi Shimbo

116 total papers · 1.8k total citations
52 papers, 893 citations indexed

About

Masashi Shimbo is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Masashi Shimbo has authored 52 papers receiving a total of 893 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 12 papers in Statistical and Nonlinear Physics and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Masashi Shimbo's work include Topic Modeling (24 papers), Advanced Graph Neural Networks (16 papers) and Natural Language Processing Techniques (15 papers). Masashi Shimbo is often cited by papers focused on Topic Modeling (24 papers), Advanced Graph Neural Networks (16 papers) and Natural Language Processing Techniques (15 papers). Masashi Shimbo collaborates with scholars based in Japan, Belgium and United States. Masashi Shimbo's co-authors include Yūji Matsumoto, Marco Saerens, Hidekazu Oiwa, Toru Ishida, Amin Mantrach, François Fouss, Katsuhiko Hayashi, Taku Kudo, Kazuo Hara and Mamoru Komachi and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and Pattern Recognition.

In The Last Decade

Masashi Shimbo

49 papers receiving 851 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Masashi Shimbo 619 201 200 118 92 52 893
Alneu de Andrade Lopes 415 0.7× 199 1.0× 177 0.9× 116 1.0× 95 1.0× 71 800
Françoise Fogelman‐Soulié 471 0.8× 134 0.7× 148 0.7× 50 0.4× 133 1.4× 32 867
Hisashi Kashima 566 0.9× 234 1.2× 144 0.7× 108 0.9× 171 1.9× 28 1.0k
Lirong Wu 407 0.7× 221 1.1× 70 0.3× 91 0.8× 60 0.7× 38 787
Mehrdad Farajtabar 363 0.6× 196 1.0× 180 0.9× 106 0.9× 95 1.0× 36 846
Christian Posse 256 0.4× 135 0.7× 147 0.7× 161 1.4× 59 0.6× 39 786
Qingquan Song 555 0.9× 238 1.2× 82 0.4× 118 1.0× 42 0.5× 23 1.0k
Qing Cui 491 0.8× 195 1.0× 208 1.0× 144 1.2× 57 0.6× 36 872
Weiwei Liu 559 0.9× 437 2.2× 84 0.4× 116 1.0× 79 0.9× 67 902
Xiaohua Hu 677 1.1× 119 0.6× 94 0.5× 323 2.7× 199 2.2× 94 1.0k

Countries citing papers authored by Masashi Shimbo

Since Specialization
Citations

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

Fields of papers citing papers by Masashi Shimbo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Masashi Shimbo. 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 Masashi Shimbo. The network helps show where Masashi Shimbo may publish in the future.

Co-authorship network of co-authors of Masashi Shimbo

This figure shows the co-authorship network connecting the top 25 collaborators of Masashi Shimbo. A scholar is included among the top collaborators of Masashi Shimbo 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 Masashi Shimbo. Masashi Shimbo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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