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.
Zero-suppressed BDDs for set manipulation in combinatorial problems
Countries citing papers authored by Shin-ichi Minato
Since
Specialization
Citations
This map shows the geographic impact of Shin-ichi Minato'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 Shin-ichi Minato with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shin-ichi Minato more than expected).
Fields of papers citing papers by Shin-ichi Minato
This network shows the impact of papers produced by Shin-ichi Minato. 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 Shin-ichi Minato. The network helps show where Shin-ichi Minato may publish in the future.
Co-authorship network of co-authors of Shin-ichi Minato
This figure shows the co-authorship network connecting the top 25 collaborators of Shin-ichi Minato.
A scholar is included among the top collaborators of Shin-ichi Minato 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 Shin-ichi Minato. Shin-ichi Minato is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Minato, Shin-ichi, et al.. (2018). Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams. International Conference on Artificial Intelligence and Statistics. 585–594.2 indexed citations
Tsuda, Koji, et al.. (2013). Compact complete inverted files for texts and directed acyclic graphs based on sequence binary decision diagrams. 157–167.1 indexed citations
12.
Yoshizawa, Shingo, et al.. (2011). High-speed string and regular expression matching on FPGA. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 457–463.7 indexed citations
13.
Kameya, Yoshitaka, et al.. (2010). An EM algorithm on BDDs with order encoding for logic-based probabilistic models. Asian Conference on Machine Learning. 161–176.2 indexed citations
14.
Minato, Shin-ichi, Ken Satoh, & Taisuke Sato. (2007). Compiling Bayesian networks by symbolic probability calculation based on zero-suppressed BDDs. Tokyo Tech Research Repository (Tokyo Institute of Technology). 2550–2555.17 indexed citations
Minato, Shin-ichi. (1993). BEM-II: An Arithmetic Boolean Expression Manipulator Using BDDs (Special Section on VLSI Design and CAD Algorithms). IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 76(10). 1721–1729.6 indexed citations
19.
Minato, Shin-ichi. (1993). Fast Generation of Prime-Irredundant Covers from Binary Decision Diagrams. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 76(6). 967–973.25 indexed citations
20.
Minato, Shin-ichi. (1992). Minimum-Width Method of Variable Ordering for Binary Decision Diagrams. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 392–399.15 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.