Masashi Shimbo

1.8k total citations
52 papers, 896 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 896 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, Katsuhiko Hayashi, François Fouss, Taku Kudo, Kazuo Hara and Luh Yen 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 854 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Masashi Shimbo Japan 17 620 203 200 119 92 52 896
Mehrdad Farajtabar United States 19 368 0.6× 199 1.0× 181 0.9× 107 0.9× 95 1.0× 37 860
Fernando Silva Portugal 17 349 0.6× 129 0.6× 206 1.0× 102 0.9× 179 1.9× 74 870
Françoise Fogelman‐Soulié France 16 470 0.8× 133 0.7× 150 0.8× 51 0.4× 133 1.4× 32 869
Petar Veličković United Kingdom 11 388 0.6× 142 0.7× 77 0.4× 51 0.4× 218 2.4× 28 910
Fragkiskos D. Malliaros France 14 400 0.6× 128 0.6× 597 3.0× 127 1.1× 123 1.3× 40 977
Ali Karcı Türkiye 14 462 0.7× 88 0.4× 96 0.5× 123 1.0× 41 0.4× 107 869
Yilin Shen United States 19 487 0.8× 211 1.0× 328 1.6× 249 2.1× 27 0.3× 103 1.3k
Luh Yen Belgium 10 203 0.3× 80 0.4× 220 1.1× 52 0.4× 85 0.9× 12 470
Hao Yang China 14 794 1.3× 186 0.9× 177 0.9× 232 1.9× 46 0.5× 90 1.1k

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

20 of 20 papers shown
1.
Shigeto, Yutaro, et al.. (2023). Learning Decorrelated Representations Efficiently Using Fast Fourier Transform. 2052–2060. 2 indexed citations
2.
Hayashi, Katsuhiko, et al.. (2021). Binarized Embeddings for Fast, Space-Efficient Knowledge Graph Completion. IEEE Transactions on Knowledge and Data Engineering. 1–1. 9 indexed citations
3.
4.
Hayashi, Katsuhiko & Masashi Shimbo. (2019). A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs. 2422–2430. 1 indexed citations
5.
Hayashi, Katsuhiko, et al.. (2018). Data-Dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 7 indexed citations
6.
7.
Kivimäki, Ilkka, Masashi Shimbo, & Marco Saerens. (2013). Developments in the theory of randomized shortest paths with a comparison of graph node distances. Physica A Statistical Mechanics and its Applications. 393. 600–616. 46 indexed citations
8.
Suzuki, Ikumi, Kazuo Hara, Masashi Shimbo, Marco Saerens, & Kenji Fukumizu. (2013). Centering Similarity Measures to Reduce Hubs. 613–623. 12 indexed citations
9.
Shimbo, Masashi, et al.. (2011). HITS-based Seed Selection and Stop List Construction for Bootstrapping. Meeting of the Association for Computational Linguistics. 30–36. 5 indexed citations
10.
Shimbo, Masashi, et al.. (2011). Using the Mutual k-Nearest Neighbor Graphs for Semi-supervised Classification on Natural Language Data. 154–162. 40 indexed citations
11.
Komachi, Mamoru, Taku Kudo, Masashi Shimbo, & Yūji Matsumoto. (2010). Semantic Drift in Espresso-style Bootstrapping: Graph-theoretic Analysis and Evaluation in Word Sense Disambiguation. Transactions of the Japanese Society for Artificial Intelligence. 25(2). 233–242. 2 indexed citations
12.
Mantrach, Amin, et al.. (2010). Semi-supervised classification and betweenness computation on large, sparse, directed graphs. Pattern Recognition. 44(6). 1212–1224. 25 indexed citations
13.
Mantrach, Amin, et al.. (2009). The Sum-over-Paths Covariance Kernel: A Novel Covariance Measure between Nodes of a Directed Graph. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(6). 1112–1126. 35 indexed citations
14.
Shimbo, Masashi, et al.. (2008). Generic Text Summarization Using Probabilistic Latent Semantic Indexing. International Joint Conference on Natural Language Processing. 133–140. 16 indexed citations
15.
Shimbo, Masashi & Kazuo Hara. (2007). A Discriminative Learning Model for Coordinate Conjunctions. Empirical Methods in Natural Language Processing. 610–619. 21 indexed citations
16.
Shimbo, Masashi, et al.. (2005). Application of kernels to link analysis. 586–592. 49 indexed citations
17.
Shimbo, Masashi, et al.. (2004). Semi - supervised sentence classification for MEDLINE documents. Medical Entomology and Zoology. 104(125). 141–146. 7 indexed citations
18.
Shimbo, Masashi, et al.. (2004). A Kernel-based Account of Bibliometric Measures. Transactions of the Japanese Society for Artificial Intelligence. 19. 530–539. 1 indexed citations
19.
Shimbo, Masashi & Toru Ishida. (2000). Towards real-time search with inadmissible heuristics. European Conference on Artificial Intelligence. 609–613. 6 indexed citations
20.
Ishida, Toru & Masashi Shimbo. (1996). Improving the learning efficiencies of realtime search. National Conference on Artificial Intelligence. 305–310. 20 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.

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