Yuhan Quan

1.1k total citations · 1 hit paper
7 papers, 498 citations indexed

About

Yuhan Quan is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yuhan Quan has authored 7 papers receiving a total of 498 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Information Systems, 5 papers in Artificial Intelligence and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yuhan Quan's work include Recommender Systems and Techniques (5 papers), Advanced Graph Neural Networks (3 papers) and Machine Learning and Data Classification (2 papers). Yuhan Quan is often cited by papers focused on Recommender Systems and Techniques (5 papers), Advanced Graph Neural Networks (3 papers) and Machine Learning and Data Classification (2 papers). Yuhan Quan collaborates with scholars based in China, Singapore and United States. Yuhan Quan's co-authors include Depeng Jin, Xiangnan He, Yong Li, Chen Gao, Jingtao Ding, Nian Li, Yinfeng Li, Yu Zheng, Jinghua Piao and Jianxin Chang and has published in prestigious journals such as ACM Transactions on Information Systems, National University of Singapore and arXiv (Cornell University).

In The Last Decade

Yuhan Quan

7 papers receiving 489 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2023 312 citations Chen Gao, Yu Zheng et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yuhan Quan China	5	344	342	109	61	53	7	498
Yu Lei Hong Kong	11	261 0.8×	310 0.9×	112 1.0×	86 1.4×	87 1.6×	15	481
Xiaochi Wei China	15	403 1.2×	282 0.8×	179 1.6×	44 0.7×	36 0.7×	28	642
Zhiping Gu China	7	269 0.8×	379 1.1×	81 0.7×	86 1.4×	49 0.9×	9	413
Yuhao Yang China	10	509 1.5×	477 1.4×	120 1.1×	65 1.1×	64 1.2×	15	661
Zihan Lin China	9	459 1.3×	533 1.6×	129 1.2×	102 1.7×	58 1.1×	32	655
Ali Elkahky United States	6	431 1.3×	471 1.4×	210 1.9×	78 1.3×	72 1.4×	10	661
Yeon-Chang Lee South Korea	12	266 0.8×	254 0.7×	131 1.2×	52 0.9×	44 0.8×	35	429
Wendi Ji China	9	246 0.7×	254 0.7×	66 0.6×	66 1.1×	26 0.5×	18	395
Jiahui Liu China	6	234 0.7×	365 1.1×	128 1.2×	71 1.2×	71 1.3×	18	508

Countries citing papers authored by Yuhan Quan

Since Specialization

Citations

This map shows the geographic impact of Yuhan Quan'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 Yuhan Quan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuhan Quan more than expected).

Fields of papers citing papers by Yuhan Quan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuhan Quan. 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 Yuhan Quan. The network helps show where Yuhan Quan may publish in the future.

Co-authorship network of co-authors of Yuhan Quan

This figure shows the co-authorship network connecting the top 25 collaborators of Yuhan Quan. A scholar is included among the top collaborators of Yuhan Quan 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 Yuhan Quan. Yuhan Quan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Zhao, Huan, et al.. (2024). Customizing Graph Neural Network for CAD Assembly Recommendation. 1746–1757. 1 indexed citations

2.

Gao, Chen, Yu Zheng, Nian Li, et al.. (2023). A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. 1(1). 1–51. 312 indexed citations breakdown →

3.

Quan, Yuhan, Jingtao Ding, Chen Gao, et al.. (2023). Alleviating Video-length Effect for Micro-video Recommendation. ACM Transactions on Information Systems. 42(2). 1–24. 7 indexed citations

4.

Quan, Yuhan, Jingtao Ding, Chen Gao, et al.. (2023). Robust Preference-Guided Denoising for Graph based Social Recommendation. arXiv (Cornell University). 1097–1108. 39 indexed citations

5.

Ding, Jingtao, Yuhan Quan, Quanming Yao, Yong Li, & Depeng Jin. (2020). Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering. arXiv (Cornell University). 33. 1094–1105. 1 indexed citations

6.

Ding, Jingtao, Yuhan Quan, Xiangnan He, Yong Li, & Depeng Jin. (2019). Reinforced Negative Sampling for Recommendation with Exposure Data. 2230–2236. 77 indexed citations

7.

Ding, Jingtao, Guanghui Yu, Xiangnan He, et al.. (2018). Improving Implicit Recommender Systems with View Data. National University of Singapore. 3343–3349. 61 indexed 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.

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