Nae-Fang Twu
About
In The Last Decade
Nae-Fang Twu
30 papers receiving 497 citations
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late) cites · hero ref
| Name | h | Career | Trend | Papers | Cites | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Nae-Fang Twu Taiwan | 13 | 196 | 190 | 165 | 115 | 100 | 30 | 508 | ||
| Erin K. Crane United States | 13 | 161 0.8× | 205 1.1× | 126 0.8× | 50 0.4× | 109 1.1× | 38 | 535 | ||
| Katharina Prieske Germany | 15 | 123 0.6× | 91 0.5× | 149 0.9× | 167 1.5× | 165 1.6× | 49 | 544 | ||
| Patricia C. Ewing‐Graham Netherlands | 13 | 95 0.5× | 106 0.6× | 192 1.2× | 181 1.6× | 122 1.2× | 39 | 528 | ||
| Ross Barner United States | 12 | 131 0.7× | 247 1.3× | 112 0.7× | 76 0.7× | 156 1.6× | 14 | 554 | ||
| Ramya P. Masand United States | 12 | 246 1.3× | 267 1.4× | 192 1.2× | 72 0.6× | 128 1.3× | 37 | 664 | ||
| Hans‐Georg Strauß Germany | 11 | 219 1.1× | 102 0.5× | 171 1.0× | 157 1.4× | 146 1.5× | 24 | 486 | ||
| Tsutomu Yuminamochi Japan | 12 | 158 0.8× | 113 0.6× | 180 1.1× | 173 1.5× | 106 1.1× | 22 | 458 | ||
| Kazuya Ariyoshi Japan | 11 | 354 1.8× | 356 1.9× | 91 0.6× | 103 0.9× | 96 1.0× | 34 | 629 | ||
| Pascasio Aguirre United States | 11 | 163 0.8× | 279 1.5× | 225 1.4× | 243 2.1× | 166 1.7× | 12 | 668 | ||
| Natalie Banet United States | 11 | 56 0.3× | 84 0.4× | 88 0.5× | 59 0.5× | 152 1.5× | 33 | 519 |
Countries citing papers authored by Nae-Fang Twu
This map shows the geographic impact of Nae-Fang Twu'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 Nae-Fang Twu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nae-Fang Twu more than expected).
Fields of papers citing papers by Nae-Fang Twu
This network shows the impact of papers produced by Nae-Fang Twu. 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 Nae-Fang Twu. The network helps show where Nae-Fang Twu may publish in the future.
Co-authorship network of co-authors of Nae-Fang Twu
This figure shows the co-authorship network connecting the top 25 collaborators of Nae-Fang Twu. A scholar is included among the top collaborators of Nae-Fang Twu 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 Nae-Fang Twu. Nae-Fang Twu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
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.