Marjorie Valix
About
In The Last Decade
Marjorie Valix
73 papers receiving 3.4k citations
Hit Papers
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late) cites · hero ref
| Name | h | Career | Trend | Papers | Cites | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Marjorie Valix Australia | 33 | 1.4k | 1.2k | 1.2k | 675 | 509 | 75 | 3.6k | ||
| Reyad Shawabkeh Saudi Arabia | 30 | 924 0.7× | 480 0.4× | 982 0.8× | 337 0.5× | 497 1.0× | 102 | 3.3k | ||
| Baicang Liu China | 37 | 1.0k 0.7× | 1.3k 1.1× | 2.2k 1.9× | 504 0.7× | 201 0.4× | 112 | 3.9k | ||
| Mohammad Al-Harahsheh Jordan | 32 | 1.5k 1.0× | 1.2k 1.0× | 929 0.8× | 834 1.2× | 128 0.3× | 96 | 3.8k | ||
| Amedeo Lancia Italy | 42 | 1.6k 1.1× | 1.1k 0.9× | 1.4k 1.2× | 428 0.6× | 207 0.4× | 163 | 4.9k | ||
| Suzylawati Ismail Malaysia | 33 | 958 0.7× | 1.2k 1.0× | 2.0k 1.7× | 491 0.7× | 229 0.4× | 110 | 3.9k | ||
| Alireza Pendashteh Iran | 23 | 368 0.3× | 778 0.7× | 1.8k 1.5× | 476 0.7× | 694 1.4× | 50 | 3.7k | ||
| Mithat Yüksel Türkiye | 44 | 1.1k 0.8× | 2.9k 2.4× | 2.5k 2.1× | 1.4k 2.1× | 109 0.2× | 154 | 6.0k | ||
| Haiqing Chang China | 38 | 684 0.5× | 1.4k 1.1× | 2.8k 2.4× | 497 0.7× | 170 0.3× | 125 | 3.9k | ||
| Haobo Hou China | 44 | 872 0.6× | 971 0.8× | 1.5k 1.2× | 1.0k 1.5× | 109 0.2× | 164 | 5.8k | ||
| Jan Yperman Belgium | 34 | 626 0.4× | 1.6k 1.3× | 574 0.5× | 314 0.5× | 250 0.5× | 129 | 3.0k |
Countries citing papers authored by Marjorie Valix
This map shows the geographic impact of Marjorie Valix'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 Marjorie Valix with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marjorie Valix more than expected).
Fields of papers citing papers by Marjorie Valix
This network shows the impact of papers produced by Marjorie Valix. 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 Marjorie Valix. The network helps show where Marjorie Valix may publish in the future.
Co-authorship network of co-authors of Marjorie Valix
This figure shows the co-authorship network connecting the top 25 collaborators of Marjorie Valix. A scholar is included among the top collaborators of Marjorie Valix 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 Marjorie Valix. Marjorie Valix 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.