Do Transformers Really Perform Badly for Graph Representation
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doi.org/w8745429 →Countries where authors are citing Do Transformers Really Perform Badly for Graph Representation
This map shows the geographic impact of Do Transformers Really Perform Badly for Graph Representation. 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 Do Transformers Really Perform Badly for Graph Representation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Do Transformers Really Perform Badly for Graph Representation more than expected).
Fields of papers citing Do Transformers Really Perform Badly for Graph Representation
This network shows the impact of Do Transformers Really Perform Badly for Graph Representation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Do Transformers Really Perform Badly for Graph Representation.
About Do Transformers Really Perform Badly for Graph Representation
This paper, published in 2021, received 259 indexed citations . Written by Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen and Tie‐Yan Liu covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (150 citations), Computer Vision and Pattern Recognition (69 citations), Molecular Biology (48 citations), Computational Theory and Mathematics (46 citations) and Materials Chemistry (38 citations). Published in Neural Information Processing Systems.
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.
This paper is also available at doi.org/w8745429.