This map shows the geographic impact of Satoshi Tojo'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 Satoshi Tojo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Satoshi Tojo more than expected).
This network shows the impact of papers produced by Satoshi Tojo. 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 Satoshi Tojo. The network helps show where Satoshi Tojo may publish in the future.
Co-authorship network of co-authors of Satoshi Tojo
This figure shows the co-authorship network connecting the top 25 collaborators of Satoshi Tojo.
A scholar is included among the top collaborators of Satoshi Tojo 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 Satoshi Tojo. Satoshi Tojo 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.
Nakamura, Makoto, et al.. (2015). Simulation of Emergence of Local Common Languages Using Iterated Learning Model on Social Networks. 8. 374–384.1 indexed citations
2.
Hirata, Keiji, Satoshi Tojo, & Masatoshi Hamanaka. (2014). Cognitive Similarity Grounded by Tree Distance from the Analysis of K.265/300e. Lecture notes in computer science. 8905. 589–605.1 indexed citations
Miura, Yuji, Masatoshi Hamanaka, Keiji Hirata, & Satoshi Tojo. (2009). Use of Decision Tree to Detect GTTM Group Boundaries. The Journal of the Abraham Lincoln Association.5 indexed citations
5.
Hamanaka, Masatoshi, Keiji Hirata, & Satoshi Tojo. (2009). Melody Extrapolation in GTTM Approach. The Journal of the Abraham Lincoln Association. 2009.2 indexed citations
6.
Hamanaka, Masatoshi, Keiji Hirata, & Satoshi Tojo. (2008). Melody Morphing Method Based on GTTM. The Journal of the Abraham Lincoln Association. 2008.13 indexed citations
7.
Nakamura, Makoto, Takashi Hashimoto, & Satoshi Tojo. (2007). Exposure Dependent Creolization in Language Dynamics Equation. JAIST Repository.3 indexed citations
8.
Nagashima, Yoshinao, et al.. (2007). Effects of cedrol on the autonomic nervous system and survey of sleep and stress in USA. 31(3). 148–152.2 indexed citations
9.
Washio, Takashi, et al.. (2006). New Frontiers in Artificial Intelligence: Joint JSAI 2005 Workshop Post-Proceedings (Lecture Notes in Computer Science). Springer eBooks.3 indexed citations
10.
Tojo, Satoshi, et al.. (2006). Stable legal knowledge with regard to contradictory arguments. 323–328.2 indexed citations
11.
Tojo, Satoshi, et al.. (2006). Discordance Detection in Regional Ordinance: Ontology-based Validation. 111–120.3 indexed citations
12.
Nakamura, Makoto, Takashi Hashimoto, & Satoshi Tojo. (2003). The Language Dynamics Equations of Population-Based Transition - A Scenario for Creolization.. International Conference on Artificial Intelligence. 2. 689–695.
13.
Matsui, Hiroki & Satoshi Tojo. (2003). Artificial Market with Intervention Agent.. Indian International Conference on Artificial Intelligence. 1255–1268.
Tojo, Satoshi, et al.. (1996). Common language acquisition by multi-agents. JAIST Repository. 96. 1–14.1 indexed citations
18.
Tojo, Satoshi, et al.. (1995). A Legal Reasoning System Based on Situation Theory. 36(1). 51–60.2 indexed citations
19.
Nitta, Katsumi, et al.. (1994). Knowledge Representation of New HELIC II.. International Conference on Lightning Protection.3 indexed citations
20.
Tojo, Satoshi, et al.. (1992). Situated Inference of Temporal Information.. Future Generation Computer Systems. 36(11). 395–404.1 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.