--- title: "Who cites Learning to Represent Programs with Graphs" description: "81 papers of the Elamur Research library cite «Learning to Represent Programs with Graphs», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Learning to Represent Programs with Graphs](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/) / cited by # Who cites «Learning to Represent Programs with Graphs» 81 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «Learning to Represent Programs with Graphs»](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/) 1. **[A Comprehensive Survey on Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/) [IEEE Transactions on Neural Networks and Learning Systems](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-neural-networks-and-learning-systems/)**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,312 citations* 2. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/) 3. [**Program Synthesis with Large Language Models arXiv:2108.07732**Jacob Austin, Augustus Odena, Maxwell Nye, et al.*2021 · 1,192 citations*](https://md.research.elamur.ai/free/en/papers/austin-2021-program-synthesis-large-language-models/) 4. [**SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2310.06770**Carlos E. Jimenez, John Yang, Alexander Wettig, et al.*2024 · 704 citations*](https://md.research.elamur.ai/free/en/papers/jimenez-2024-swe-bench-can-language-models/) 5. [**Show your work: Scratchpads for intermediate computation with language models***2021 · 473 citations*](https://md.research.elamur.ai/free/en/papers/al-2021-show-your-work-scratchpads-intermediate/) 6. [**GraphCodeBERT: Pre-training Code Representations with Data Flow arXiv:2009.08366**Daya Guo, Shuo Ren, Shuai Lu, et al.*2021 · 332 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/) 7. [**The Adverse Effects of Code Duplication in Machine Learning Models of Code arXiv:1812.06469**Miltiadis Allamanis*2018 · 319 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2018-adverse-effects-code-duplication-machine/) 8. [**Learning to Optimize Tensor Programs arXiv:1805.08166**Tianqi Chen, Lianmin Zheng, Eddie Yan, et al.*2019 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-learning-optimize-tensor-programs/) 9. **[A Survey of Machine Learning for Big Code and Naturalness](https://md.research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.*2017 · 167 citations* 10. [**SPoC: Search-based Pseudocode to Code arXiv:1906.04908**Sumith Kulal, Panupong Pasupat, Kartik Chandra, et al.*2019 · 96 citations*](https://md.research.elamur.ai/free/en/papers/kulal-2019-spoc-search-based-pseudocode-code/) 11. [**Learning to Prove Theorems via Interacting with Proof Assistants arXiv:1905.09381**Kaiyu Yang, Jia Deng*2019 · 90 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-learning-prove-theorems-interacting-proof/) 12. [**code2seq: Generating Sequences from Structured Representations of Code arXiv:1808.01400**Uri Alon, Shaked Brody, Omer Levy, et al.*2018 · 72 citations*](https://md.research.elamur.ai/free/en/papers/alon-2018-code2seq-generating-sequences-structured-representations/) 13. [**Stable Code Technical Report arXiv:2404.01226**Nikhil Pinnaparaju, Reshinth Adithyan, Duy Phung, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/pinnaparaju-2024-stable-code-technical-report/) 14. **[From Logical to Computational Sparsity: Structure-Aware Block-Sparse Attention for Long-Code Completion](https://md.research.elamur.ai/free/en/papers/wang-2026-logical-computational-sparsity-structure-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanli Wang, Yanlin Wang, Bowen Zhang, et al.*2026* 15. **[LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval](https://md.research.elamur.ai/free/en/papers/wang-2026-ledger-scaling-agentic-document-editing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hang Wang, Utkarsh Garg, Reza Davari, et al.*2026* 16. **[Review and Performance Comparison of Deep Knowledge Tracing Models (深度知识追踪模型综述和性能比较)](https://md.research.elamur.ai/free/en/papers/wang-2022-performance-comparison-deep/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yu Wang (王宇), Mengxia Zhu (朱梦霞), Shanghui Yang (杨尚辉), et al.*2022* 65 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/) — Learning to Represent Programs with Graphs