Hidekazu Oiwa

471 total citations
13 papers, 273 citations indexed

About

Hidekazu Oiwa is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Mechanics. According to data from OpenAlex, Hidekazu Oiwa has authored 13 papers receiving a total of 273 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 4 papers in Management Science and Operations Research and 2 papers in Computational Mechanics. Recurrent topics in Hidekazu Oiwa's work include Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers) and Sparse and Compressive Sensing Techniques (2 papers). Hidekazu Oiwa is often cited by papers focused on Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers) and Sparse and Compressive Sensing Techniques (2 papers). Hidekazu Oiwa collaborates with scholars based in Japan. Hidekazu Oiwa's co-authors include Yūji Matsumoto, Masashi Shimbo, Hiroshi Nakagawa, Issei Sato, Ryohei Fujimaki, Yusuke Miyao, Behzad Golshan, Yasushi Kamimura, Takashi YOKOYAMA and Wang-Chiew Tan and has published in prestigious journals such as Proceedings of the VLDB Endowment, Pacific-Basin Finance Journal and Science China Information Sciences.

In The Last Decade

Hidekazu Oiwa

11 papers receiving 248 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hidekazu Oiwa Japan 6 215 57 36 24 21 13 273
Siming He China 5 120 0.6× 53 0.9× 44 1.2× 33 1.4× 21 1.0× 11 235
Yiwei Wang Singapore 7 203 0.9× 46 0.8× 50 1.4× 55 2.3× 13 0.6× 14 262
Yizhen Zheng Australia 11 217 1.0× 44 0.8× 90 2.5× 36 1.5× 22 1.0× 20 294
Yiyang Gu China 8 230 1.1× 86 1.5× 47 1.3× 44 1.8× 19 0.9× 15 341
Anders Holst Sweden 7 341 1.6× 36 0.6× 33 0.9× 8 0.3× 23 1.1× 23 441
Doudou Lin China 5 192 0.9× 88 1.5× 41 1.1× 40 1.7× 10 0.5× 7 295
Romain Hennequin France 9 132 0.6× 111 1.9× 46 1.3× 32 1.3× 6 0.3× 29 354
J. Austin United Kingdom 8 124 0.6× 106 1.9× 36 1.0× 13 0.5× 35 1.7× 35 258
Lingyun Song China 10 159 0.7× 120 2.1× 28 0.8× 11 0.5× 6 0.3× 25 257
Faisal Anwer India 10 93 0.4× 104 1.8× 46 1.3× 17 0.7× 25 1.2× 25 256

Countries citing papers authored by Hidekazu Oiwa

Since Specialization
Citations

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

Fields of papers citing papers by Hidekazu Oiwa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hidekazu Oiwa

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

All Works

13 of 13 papers shown
1.
Wang, Xiaolan, Behzad Golshan, Alon Halevy, et al.. (2018). Scalable semantic querying of text. Proceedings of the VLDB Endowment. 11(9). 961–974. 5 indexed citations
2.
Oiwa, Hidekazu, et al.. (2018). Knowledge Base Completion with Out-of-Knowledge-Base Entities: A Graph Neural Network Approach. Transactions of the Japanese Society for Artificial Intelligence. 33(2). F–H72_1. 29 indexed citations
3.
4.
Sato, Issei, et al.. (2018). Mining Words in the Minds of Second Language Learners for Learner-specific Word Difficulty. Journal of Information Processing. 26(0). 267–275. 14 indexed citations
5.
Oiwa, Hidekazu, et al.. (2017). Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach. 1802–1808. 187 indexed citations
6.
Miyao, Yusuke, et al.. (2014). Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning. 1374–1384. 17 indexed citations
7.
Oiwa, Hidekazu, et al.. (2014). Feature-aware regularization for sparse online learning. Science China Information Sciences. 57(5). 1–21. 3 indexed citations
8.
Oiwa, Hidekazu & Ryohei Fujimaki. (2014). Partition-wise Linear Models. arXiv (Cornell University). 27. 3527–3535. 11 indexed citations
9.
Sato, Issei, et al.. (2013). Understanding seed selection in bootstrapping. 44–52. 1 indexed citations
10.
Oiwa, Hidekazu, et al.. (2012). The Economic Impact of Herd Behavior in the Japanese Loan Market. Pacific-Basin Finance Journal. 20(4). 600–613.
11.
Oiwa, Hidekazu, et al.. (2012). Healing Truncation Bias: Self-Weighted Truncation Framework for Dual Averaging. 7. 575–584. 1 indexed citations
12.
Kamimura, Yasushi, Takashi YOKOYAMA, Hidekazu Oiwa, Keiichi Edagawa, & Ichiro Yonenaga. (2009). Electrical conduction along dislocations in plastically deformed GaN. IOP Conference Series Materials Science and Engineering. 3. 12010–12010. 4 indexed citations
13.
Oiwa, Hidekazu, H Matsunaga, H Makuuchi, et al.. (1991). [Mitral valve disease with pulmonary hypertension: two surgical cases].. PubMed. 25. 175–84; discussion 185. 1 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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