Matias Casás‐Selves
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Co-authorship network of co-authors of Matias Casás‐Selves
This figure shows the co-authorship network connecting the top 25 collaborators of Matias Casás‐Selves. A scholar is included among the top collaborators of Matias Casás‐Selves 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 Matias Casás‐Selves. Matias Casás‐Selves is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
Matias Casás‐Selves
9 papers receiving 529 citations
Fields of papers citing papers by Matias Casás‐Selves
This network shows the impact of papers produced by Matias Casás‐Selves. 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 Matias Casás‐Selves. The network helps show where Matias Casás‐Selves may publish in the future.
Countries citing papers authored by Matias Casás‐Selves
This map shows the geographic impact of Matias Casás‐Selves'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 Matias Casás‐Selves with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matias Casás‐Selves more than expected).
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.