A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

312 indexed citations
published 2023

Countries where authors are citing A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Specialization
Citations

This map shows the geographic impact of A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. 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 A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions more than expected).

Fields of papers citing A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions.

About A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

This paper, published in 2023, received 312 indexed citations . Written by Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He and Yong Li covering the research area of Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (220 citations), Information Systems (179 citations) and Computer Vision and Pattern Recognition (61 citations).

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/10.1145/3568022.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026