Tsuyoshi Murata

3.7k total citations
131 papers, 2.4k citations indexed

About

Tsuyoshi Murata is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Tsuyoshi Murata has authored 131 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Statistical and Nonlinear Physics, 57 papers in Artificial Intelligence and 28 papers in Molecular Biology. Recurrent topics in Tsuyoshi Murata's work include Complex Network Analysis Techniques (73 papers), Advanced Graph Neural Networks (34 papers) and Opinion Dynamics and Social Influence (24 papers). Tsuyoshi Murata is often cited by papers focused on Complex Network Analysis Techniques (73 papers), Advanced Graph Neural Networks (34 papers) and Opinion Dynamics and Social Influence (24 papers). Tsuyoshi Murata collaborates with scholars based in Japan, United States and China. Tsuyoshi Murata's co-authors include Xin Liu, Eva Degerman, M. Taira, Vincent C. Manganiello, Per Belfrage, Vincent Manganiello, Xin Liu, Hitoshi Ishizawa, Faiyaz Ahmad and Kasumi Shimizu and has published in prestigious journals such as Journal of Clinical Investigation, SHILAP Revista de lepidopterología and IEEE Transactions on Industrial Electronics.

In The Last Decade

Tsuyoshi Murata

120 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tsuyoshi Murata Japan 25 901 744 691 306 237 131 2.4k
Nataša Pržulj United Kingdom 37 1.0k 1.1× 669 0.9× 4.4k 6.3× 117 0.4× 184 0.8× 92 6.0k
Michael T. Schaub Germany 23 605 0.7× 373 0.5× 1.3k 1.8× 34 0.1× 163 0.7× 64 2.5k
Xiaoliang Chen China 30 157 0.2× 459 0.6× 1.4k 2.0× 141 0.5× 73 0.3× 208 3.0k
Michael D. Lieberman United States 30 317 0.4× 491 0.7× 337 0.5× 515 1.7× 215 0.9× 57 3.6k
Lin Gao China 33 418 0.5× 389 0.5× 2.1k 3.0× 79 0.3× 167 0.7× 154 3.2k
Jian Tang China 26 266 0.3× 1.4k 1.9× 505 0.7× 228 0.7× 95 0.4× 94 2.9k
Mehmet Koyutürk United States 30 131 0.1× 307 0.4× 1.7k 2.4× 155 0.5× 125 0.5× 122 2.7k
Lin Gui China 34 59 0.1× 1.8k 2.4× 756 1.1× 200 0.7× 190 0.8× 333 4.4k
Wei Wei Zhang China 29 456 0.5× 291 0.4× 674 1.0× 25 0.1× 612 2.6× 189 2.5k
Pierangelo Veltri Italy 23 61 0.1× 629 0.8× 581 0.8× 183 0.6× 325 1.4× 237 2.2k

Countries citing papers authored by Tsuyoshi Murata

Since Specialization
Citations

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

Fields of papers citing papers by Tsuyoshi Murata

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tsuyoshi Murata

This figure shows the co-authorship network connecting the top 25 collaborators of Tsuyoshi Murata. A scholar is included among the top collaborators of Tsuyoshi Murata 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 Tsuyoshi Murata. Tsuyoshi Murata 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.
Ding, Yue, Xin Xin, Yuxiang Lu, et al.. (2025). Towards Personalized Federated Multi-Scenario Multi-Task Recommendation. 429–438. 1 indexed citations
2.
Liu, Xin, et al.. (2024). Future-proofing class-incremental learning. Machine Vision and Applications. 36(1).
3.
Liu, Xin, et al.. (2023). Predicting potential real-time donations in YouTube live streaming services via continuous-time dynamic graphs. Machine Learning. 113(4). 2093–2127. 1 indexed citations
4.
Murata, Tsuyoshi. (2019). Deep Learning Approaches for Eruption Prediction of Sakurajima. Japan Geoscience Union.
5.
Murata, Tsuyoshi, et al.. (2017). Predicting relations of embedded RDF entities by Deep Neural Network.. 1 indexed citations
6.
Murata, Tsuyoshi, et al.. (2015). New Frontiers in Artificial Intelligence JSAI-isAI 2014 Workshops, LENLS, JURISIN, and GABA Kanagawa, Japan, October 27-28, 2014 Revised Selected Papers. CERN Document Server (European Organization for Nuclear Research). 2 indexed citations
7.
Liu, Xin, Tsuyoshi Murata, & Ken Wakita. (2014). Detecting network communities beyond assortativity-related attributes. Physical Review E. 90(1). 12806–12806. 5 indexed citations
8.
Murata, Tsuyoshi, et al.. (2013). Recent Large Graph Visualization Tools : A Review. Journal of information processing. 8(4). 944–960. 5 indexed citations
9.
Murata, Tsuyoshi, Junko Hieda, Nagahiro Saito, & Osamu Takai. (2012). Preparation and wettability examinations of transparent SiO2binder-added MgF2nanoparticle coatings covered with fluoro-alkyl silane self-assembled monolayer. Applied Optics. 51(13). 2298–2298. 3 indexed citations
10.
Murata, Tsuyoshi. (2011). A New Tripartite Modularity for Detecting Communities. Journal of information processing. 6(2). 572–579.
11.
Murata, Tsuyoshi. (2009). Detecting Communities from Bipartite Networks Based on Bipartite Modularities. 50–57. 31 indexed citations
12.
Ishizawa, Hitoshi, et al.. (2008). Preparation of MgF_2-SiO_2 thin films with a low refractive index by a solgel process. Applied Optics. 47(13). C200–C200. 15 indexed citations
13.
Murata, Tsuyoshi, et al.. (2006). Extraction and Visualization of Web Users' Interests Using Site-Keyword Graphs. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics. 18(5). 701–710.
14.
Nakagawa, Toshiyuki, et al.. (2001). An immuno-light- and electron-microscopic study of the expression of bone morphogenetic protein-2 during the process of ectopic bone formation in the rat. Archives of Oral Biology. 46(5). 403–411. 16 indexed citations
15.
Murata, Tsuyoshi & Masamichi Shimura. (1996). Machine discovery based on numerical data generated in computer experiments. National Conference on Artificial Intelligence. 41(7). 737–742. 1 indexed citations
16.
Reinhardt, Robert, E Chin, Jianqing Zhou, et al.. (1995). Distinctive anatomical patterns of gene expression for cGMP-inhibited cyclic nucleotide phosphodiesterases.. Journal of Clinical Investigation. 95(4). 1528–1538. 137 indexed citations
17.
Manganiello, Vincent C., Tsuyoshi Murata, M. Taira, Per Belfrage, & Eva Degerman. (1995). Diversity in Cyclic Nucleotide Phosphodiesterase Isoenzyme Families. Archives of Biochemistry and Biophysics. 322(1). 1–13. 212 indexed citations
18.
Murata, Tsuyoshi, Masami Mizutani, & Masamichi Shimura. (1994). A discovery system for trigonometric functions. National Conference on Artificial Intelligence. 645–650. 4 indexed citations
19.
Murata, Tsuyoshi, Hiroshige Hibasami, Takanobu Tagawa, & K Nakashima. (1992). Methylglyoxal bis(butylamidinohydrazone) exhibits antitumor effect on human malignant melanoma cells but reduces the antitumor action of cisplatin. Anti-Cancer Drugs. 3(6). 683–686. 2 indexed citations
20.
Hibasami, Hiroshige, T Tsukada, Rikio Suzuki, et al.. (1991). 15-Deoxyspergualin, an antiproliferative agent for human and mouse leukemia cells shows inhibitory effects on the synthetic pathway of polyamines.. PubMed. 11(1). 325–30. 12 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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