Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Countries citing papers authored by Hiroshi Motoda
Since
Specialization
Citations
This map shows the geographic impact of Hiroshi Motoda'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 Hiroshi Motoda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hiroshi Motoda more than expected).
This network shows the impact of papers produced by Hiroshi Motoda. 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 Hiroshi Motoda. The network helps show where Hiroshi Motoda may publish in the future.
Co-authorship network of co-authors of Hiroshi Motoda
This figure shows the co-authorship network connecting the top 25 collaborators of Hiroshi Motoda.
A scholar is included among the top collaborators of Hiroshi Motoda 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 Hiroshi Motoda. Hiroshi Motoda 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.
Kryszkiewicz, Marzena, et al.. (2014). Rough Sets and Intelligent Systems Paradigms: Second International Conference, RSEISP 2014, Granada and Madrid, Spain, July 9-13, 2014. Springer eBooks.1 indexed citations
2.
Saito, Kazumi, Masahiro Kimura, Kouzou Ohara, & Hiroshi Motoda. (2010). Generative Models of Information Diffusion with Asynchronous Timedelay. Asian Conference on Machine Learning. 193–208.14 indexed citations
Kimura, Masahiro, Kazumi Saito, & Hiroshi Motoda. (2008). Minimizing the spread of contamination by blocking links in a network. National Conference on Artificial Intelligence. 1175–1180.71 indexed citations
Motoda, Hiroshi, et al.. (2006). A study on rough set-aided feature selection for automatic web-page classification. Web Intelligence and Agent Systems An International Journal. 4(4). 431–441.27 indexed citations
7.
Tsumoto, Shusaku, Takahira Yamaguchi, Masayuki Numao, & Hiroshi Motoda. (2005). Active Mining: Second International Workshop, AM 2003, Maebashi, Japan, October 28, 2003, Revised Selected Papers (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). Springer eBooks.4 indexed citations
8.
Hoffmann, Achim, Hiroshi Motoda, & Tobias Scheffer. (2005). Proceedings of the 8th international conference on Discovery Science.6 indexed citations
9.
Yada, Katsutoshi, et al.. (2004). Knowledge Discovery from Consumer Behavior in an Alcohol Market by Using Graph Mining Technique. 2004(125). 111–116.1 indexed citations
10.
Liu, Huan, Lei Yu, Manoranjan Dash, & Hiroshi Motoda. (2003). Active feature selection using classes. Knowledge Discovery and Data Mining. 474–485.11 indexed citations
11.
Motoda, Hiroshi, et al.. (2002). Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction.
12.
Liu, Huan, Hiroshi Motoda, & Lei Yu. (2002). Feature Selection with Selective Sampling. International Conference on Machine Learning. 395–402.56 indexed citations
Washio, Takashi, et al.. (2000). Enhancing the Plausibility of Law Equation Discovery. International Conference on Machine Learning. 1127–1134.7 indexed citations
16.
Nishioka, Shingo, Atsuo Kawaguchi, & Hiroshi Motoda. (1996). Process-labeled kernel profiling: a new facility to profile system activities. USENIX Annual Technical Conference. 24–24.1 indexed citations
17.
Suwa, Masaki & Hiroshi Motoda. (1995). Operator Schemata Acquisition based on Recognition Propagation Rule. Cognitive Studies | Études cognitives. 2(4).
18.
Narayanan, N. Hari, Masaki Suwa, & Hiroshi Motoda. (1994). How things appear to work: predicting behaviors from device diagrams. National Conference on Artificial Intelligence. 1161–1167.11 indexed citations
19.
Motoda, Hiroshi, Naoyuki Yamada, & Ken Yoshida. (1984). A Knowledge based System for Plant Diagnosis.. Future Generation Computer Systems. 582–588.9 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.