Hierarchical graph representation learning with differentiable pooling
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w7054783 →Countries where authors are citing Hierarchical graph representation learning with differentiable pooling
This map shows the geographic impact of Hierarchical graph representation learning with differentiable pooling. 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 Hierarchical graph representation learning with differentiable pooling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hierarchical graph representation learning with differentiable pooling more than expected).
Fields of papers citing Hierarchical graph representation learning with differentiable pooling
This network shows the impact of Hierarchical graph representation learning with differentiable pooling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hierarchical graph representation learning with differentiable pooling.
About Hierarchical graph representation learning with differentiable pooling
This paper, published in 2018, received 415 indexed citations . Written by Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton and Jure Leskovec covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (302 citations), Computer Vision and Pattern Recognition (135 citations) and Statistical and Nonlinear Physics (96 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/w7054783.