Xiaoni Ai
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Co-authorship network of co-authors of Xiaoni Ai
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoni Ai. A scholar is included among the top collaborators of Xiaoni Ai 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 Xiaoni Ai. Xiaoni Ai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
Xiaoni Ai
21 papers receiving 348 citations
Fields of papers citing papers by Xiaoni Ai
This network shows the impact of papers produced by Xiaoni Ai. 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 Xiaoni Ai. The network helps show where Xiaoni Ai may publish in the future.
Countries citing papers authored by Xiaoni Ai
This map shows the geographic impact of Xiaoni Ai'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 Xiaoni Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoni Ai 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.