Akihiro Inokuchi
About
In The Last Decade
Akihiro Inokuchi
25 papers receiving 651 citations
Peers
Comparison fields: 5 of 79
- Artificial Intelligence 345
- Information Systems 224
- Computer Vision and Pattern Recognition 210
- Computational Theory and Mathematics 151
- Molecular Biology 141
Countries citing papers authored by Akihiro Inokuchi
This map shows the geographic impact of Akihiro Inokuchi'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 Akihiro Inokuchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akihiro Inokuchi more than expected).
Fields of papers citing papers by Akihiro Inokuchi
This network shows the impact of papers produced by Akihiro Inokuchi. 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 Akihiro Inokuchi. The network helps show where Akihiro Inokuchi may publish in the future.
Co-authorship network of co-authors of Akihiro Inokuchi
This figure shows the co-authorship network connecting the top 25 collaborators of Akihiro Inokuchi. A scholar is included among the top collaborators of Akihiro Inokuchi 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 Akihiro Inokuchi. Akihiro Inokuchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 7 | |
| 3 | 8 | |
| 4 | 3 | |
| 5 | 19 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 1 | |
| 9 | 20 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 9 | |
| 13 | 29 | |
| 14 | Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining | 18 |
| 15 | 19 | |
| 16 | A General Framework for Mining Frequent Subgraphs from Labeled Graphs | 8 |
| 17 | Marginalized kernels between labeled graphs | 371 |
| 18 | 162 | |
| 19 | 0 | |
| 20 | 10 |
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.