---
title: "Rex Ying · Yale University — author profile and papers"
description: "Rex Ying, Yale University: 3 papers on Elamur Research (2017–2026), h-index 24 — affiliations, topics, co-authors and who cites them in our library."
canonical: "https://research.elamur.ai/free/en/authors/rex-ying/"
---
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# Rex Ying

Rex Ying has 3 papers in our library, in 2017–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Chengwei Qin](https://md.research.elamur.ai/free/en/authors/chengwei-qin/), [Irwin King](https://md.research.elamur.ai/free/en/authors/irwin-king/), [Jiahong Liu](https://md.research.elamur.ai/free/en/authors/jiahong-liu/).

3 papers in our library · 2017–2026

[OpenAlex](https://openalex.org/A5078337825) · [ORCID](https://orcid.org/0000-0002-5856-5229) · [Wikidata](https://www.wikidata.org/wiki/Q130845211) · [**h-index 24**](https://openalex.org/authors/A5078337825) · [113 works](https://openalex.org/works?filter=author.id:A5078337825) · [8,613 citations](https://openalex.org/works?filter=author.id:A5078337825&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/rex-ying/#papers) · [🔗 Cited 1,650 times in our library](https://md.research.elamur.ai/free/en/authors/rex-ying/cited-by/) · [29 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #683](https://md.research.elamur.ai/free/en/paper-stars/page/7/#star-683)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Rex+Ying) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Rex+Ying) · [dblp](https://dblp.org/search?q=Rex+Ying) · [ACL Anthology](https://aclanthology.org/search/?q=Rex+Ying)

## Affiliations

[🎓**Yale University** New Haven · United States 2022–2026 · 101 papers*current*](https://md.research.elamur.ai/free/en/universities/yale-university/) · [🎓**Stanford University** Stanford · United States 2015–2022 · 28 papers](https://md.research.elamur.ai/free/en/universities/stanford-university/) · [🎓**Pennsylvania State University** State College · United States 2021–2022 · 2 papers](https://md.research.elamur.ai/free/en/universities/pennsylvania-state-university/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 12 The field of science this work belongs to (OpenAlex fields).[Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Physics and Astronomy · 1 work](https://md.research.elamur.ai/free/en/shelves/physics-and-astronomy/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Statistical and Nonlinear Physics · 1 work](https://md.research.elamur.ai/free/en/shelves/physics-and-astronomy/statistical-and-nonlinear-physics/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Health Informatics · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/health-informatics/) · [Health Information Management · 1 work](https://md.research.elamur.ai/free/en/shelves/health-professions/health-information-management/) · [Health Professions · 1 work](https://md.research.elamur.ai/free/en/shelves/health-professions/) · [Medicine · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/)

🧭 Topics 4 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Information retrieval & search · 1 work](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/topics/question-answering/)

## Works by year

### 2020s

[**2026** 1 paper](https://md.research.elamur.ai/free/en/authors/rex-ying/year/2026/) · [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/rex-ying/year/2025/)

### 2010s

[**2017** 1 paper](https://md.research.elamur.ai/free/en/authors/rex-ying/year/2017/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Inductive Representation Learning on Large Graphs](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)** *(2017 · 4,795 citations)* Most earlier node-embedding techniques, such as factorization or random-walk objectives, fit a separate vector for every node of one fixed graph.
2. **[Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/li-2025-implicit-reasoning-large-language-models/)** *(2025 · 10 citations)* Chain-of-Thought prompting makes LLMs write out every intermediate step, which inflates output length, latency and cost.
3. **[Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA](https://md.research.elamur.ai/free/en/papers/ou-2026-experience-retrieval-augmentation-electronic-health/)** *(2026 · 4 citations)* LLMs used for clinical question answering often hallucinate. Standard RAG mostly supplies generic medical facts, such as a drug's usual dose, which ignore the combination of conditions a real patient has. The authors argue that models, like experienced clinicians, benefit from looking at comparable past cases. They…

## All works

32 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. [**Inductive Representation Learning on Large Graphs arXiv:1706.02216**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,795 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
2. **[Graph Convolutional Neural Networks for Web-Scale Recommender Systems ↗](https://doi.org/10.1145/3219819.3219890) ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · no summary yet**Rex Ying et al.*2018 · 3,023 citations*
3. **[Hierarchical graph representation learning with differentiable pooling ↗](https://openalex.org/W2811124557) neural information processing systems · no summary yet**Zhitao Ying et al.*2018 · 865 citations*
4. **[GNNExplainer: Generating Explanations for Graph Neural Networks ↗](https://arxiv.org/abs/1903.03894) arXiv (Cornell University) · no summary yet**Rex Ying et al.*2019 · 685 citations*
5. **[GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models ↗](https://arxiv.org/abs/1802.08773) arXiv (Cornell University) · no summary yet**Jiaxuan You et al.*2018 · 490 citations*
6. **[Learning to Simulate Complex Physics with Graph Networks ↗](https://arxiv.org/abs/2002.09405) arXiv:2002.09405 · no summary yet**Alvaro Sanchez-Gonzalez et al.*2020 · 462 citations*
7. **[Representation Learning on Graphs: Methods and Applications ↗](https://arxiv.org/abs/1709.05584) arXiv (Cornell University) · no summary yet**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 366 citations*
8. **[Hyperbolic Graph Convolutional Neural Networks ↗](https://arxiv.org/abs/1910.12933) arXiv (Cornell University) · no summary yet**Ines Chami et al.*2019 · 291 citations*
9. **[Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation ↗](https://arxiv.org/abs/1806.02473) arXiv (Cornell University) · no summary yet**Jiaxuan You et al.*2018 · 259 citations*
10. **[Design Space for Graph Neural Networks ↗](https://arxiv.org/abs/2011.08843) arXiv (Cornell University) · no summary yet**Jiaxuan You, Rex Ying, Jure Leskovec*2020 · 176 citations*
11. **[Identity-aware Graph Neural Networks ↗](https://doi.org/10.1609/aaai.v35i12.17283) Proceedings of the AAAI Conference on Artificial Intelligence · no summary yet**Jiaxuan You et al.*2021 · 159 citations*
12. **[Multi-hop Attention Graph Neural Networks ↗](https://doi.org/10.24963/ijcai.2021/425) no summary yet**Guangtao Wang et al.*2021 · 111 citations*
13. **[Position-aware Graph Neural Networks ↗](https://arxiv.org/abs/1906.04817) arXiv (Cornell University) · no summary yet**Jiaxuan You, Rex Ying, Jure Leskovec*2019 · 94 citations*
14. **[Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification ↗](https://aclanthology.org/2021.naacl-main.229/) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yet**Xiaochen Hou et al.*2021 · 62 citations*
15. **[From Similarity to Superiority: Channel Clustering for Time Series Forecasting ↗](https://doi.org/10.52202/079017-4152) neural information processing systems · no summary yet**Jialin Chen et al.*2024 · 44 citations*
16. **[Bipartite Dynamic Representations for Abuse Detection ↗](https://doi.org/10.1145/3447548.3467141) no summary yet**Andrew Z. Wang et al.*2021 · 35 citations*
17. **[Multi-hop Attention Graph Neural Network ↗](https://arxiv.org/abs/2009.14332) arXiv (Cornell University) · no summary yet**Guangtao Wang et al.*2020 · 34 citations*
18. **[Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator ↗](https://doi.org/10.1145/3534678.3539045) ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · no summary yet**Tailin Wu et al.*2022 · 33 citations*
19. **[Improving Graph Attention Networks with Large Margin-based Constraints ↗](https://arxiv.org/abs/1910.11945) arXiv (Cornell University) · no summary yet**Guangtao Wang et al.*2019 · 25 citations*
20. **[Relational Deep Learning: Graph Representation Learning on Relational Databases ↗](https://arxiv.org/abs/2312.04615) arXiv (Cornell University) · no summary yet**Matthias Fey et al.*2023 · 23 citations*
21. **[Direct Multi-hop Attention based Graph Neural Network ↗](http://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-14332) arXiv (Cornell University) · no summary yet**Guangtao Wang et al.*2020 · 20 citations*
22. **[Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones ↗](https://arxiv.org/abs/2110.14923) arXiv (Cornell University) · no summary yet**Yushi Bai et al.*2021 · 18 citations*
23. **[MUDiff: Unified Diffusion for Complete Molecule Generation ↗](https://arxiv.org/abs/2304.14621) arXiv (Cornell University) · no summary yet**Chenqing Hua et al.*2023 · 14 citations*
24. **[Neural Distance Embeddings for Biological Sequences ↗](https://doi.org/10.17863/cam.78685) arXiv (Cornell University) · no summary yet**Gabriele Corso et al.*2021 · 12 citations*
25. [**Implicit Reasoning in Large Language Models: A Comprehensive Survey arXiv:2509.02350**Jindong Li, Yali Fu, Li Fan, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-implicit-reasoning-large-language-models/)

[See all 32 works by Rex Ying →](https://md.research.elamur.ai/free/en/authors/rex-ying/works/)

## Co-authors here

12 co-authors with a page, the most papers together first.

- [Chengwei Qin](https://md.research.elamur.ai/free/en/authors/chengwei-qin/)
- [Irwin King](https://md.research.elamur.ai/free/en/authors/irwin-king/)
- [Jiahong Liu](https://md.research.elamur.ai/free/en/authors/jiahong-liu/)
- [Jindong Li](https://md.research.elamur.ai/free/en/authors/jindong-li/)
- [Jure Leskovec](https://md.research.elamur.ai/free/en/authors/jure-leskovec/)
- [Justice Ou](https://md.research.elamur.ai/free/en/authors/justice-ou/)
- [Menglin Yang](https://md.research.elamur.ai/free/en/authors/menglin-yang/)
- [Tinglin Huang](https://md.research.elamur.ai/free/en/authors/tinglin-huang/)
- [William L. Hamilton](https://md.research.elamur.ai/free/en/authors/william-l-hamilton/)
- [Yao Shu](https://md.research.elamur.ai/free/en/authors/yao-shu/)
- [Yifei Shen](https://md.research.elamur.ai/free/en/authors/yifei-shen/)
- [Yilun Zhao](https://md.research.elamur.ai/free/en/authors/yilun-zhao/)

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- neural information processing systems ×2
- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
- ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
- Bulletin of the American Physical Society
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Proceedings of the AAAI Conference on Artificial Intelligence

## Cited by

Papers of our library cite their works 1,650 times (of the 149,439 papers whose bibliographies we read); [139 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/rex-ying/cited-by/).

Updated 1 November 2026