---
title: "Inductive Representation Learning on Large — Hamilton 2017"
description: "Most node-embedding methods learn one vector per node of a fixed graph."
authors: ["William L. Hamilton", "Rex Ying", "Jure Leskovec"]
year: 2017
venue: "arXiv:1706.02216"
source: "https://arxiv.org/abs/1706.02216"
doi: "10.48550/arxiv.1706.02216"
arxiv: "1706.02216"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/"
---
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# Inductive Representation Learning on Large Graphs

**Authors:** [William L. Hamilton](https://md.research.elamur.ai/free/en/authors/william-l-hamilton/), Rex Ying, [Jure Leskovec](https://md.research.elamur.ai/free/en/authors/jure-leskovec/) · **Year:** 2017 · **Venue:** arXiv:1706.02216 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1706.02216) · [PDF](https://arxiv.org/pdf/1706.02216) · [DOI 10.48550/arxiv.1706.02216](https://doi.org/10.48550/arxiv.1706.02216) · [arXiv 1706.02216](https://arxiv.org/abs/1706.02216) · [OpenAlex](https://openalex.org/W2624431344)

**Citations:** 4128 citations (openalex-snapshot · 2026-10-14) · [559](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most node-embedding methods learn one vector per node of a fixed graph. They cannot place nodes they did not see during training without retraining, which is a problem for graphs that keep growing (new posts, users, papers) and for moving a model to a new graph. Hamilton, Ying and Leskovec present GraphSAGE, an inductive framework. Instead of storing an embedding for each node, it learns functions that build a node's representation from its own features and a fixed-size sample of its neighbours' features, repeated over several hops. The aggregators they test are mean, LSTM, max-pooling and a GCN-style variant. The model can be trained with an unsupervised, random-walk-based loss or trained end-to-end on labels. It is evaluated on three node-classification tasks: an evolving citation graph, Reddit posts and multiple protein-protein interaction (PPI) graphs. GraphSAGE beats feature-only and transductive baselines while producing embeddings for new nodes far faster. This made the work a foundation for scalable, production-ready graph neural networks.

## Key points
- **Benchmarks:** paper subject classification on citation data, community (subreddit) prediction on Reddit, and protein-function prediction on PPI. PPI tests whether a model trained on some graphs works on entirely unseen graphs.
- **Main result:** the supervised GraphSAGE raises F1 by about 51% on average over a classifier that uses only raw node features. It also beats a strong transductive embedding baseline (DeepWalk), which is roughly 100× slower at inference on new nodes.
- **Aggregators:** the proposed LSTM and pooling aggregators improve on the GCN-inspired aggregator by about 7.4% on average. A Wilcoxon signed-rank test over 6 settings gives p ≈ 0.02. LSTM and pooling perform about the same, but pooling is roughly 2× faster.
- **Neighbourhood sampling:** sampling a fixed number of neighbours keeps memory and runtime bounded per batch while preserving most of the accuracy.
- **Theory:** if every node's features are distinct, the algorithm with K = 4 search depths can approximate any node's clustering coefficient to arbitrary precision. This shows that a feature-based model can still learn graph structure.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

559 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 549 not yet.

- [Relational inductive biases, deep learning, and graph networks](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://md.research.elamur.ai/free/en/papers/cohan-2020-specter-document-level-representation-learning/)
- [Lexicon-Enriched Graph Modeling for Arabic Document Readability Prediction](https://md.research.elamur.ai/free/en/papers/elchafei-2025-lexicon-enriched-graph-modeling-arabic/)
- [Fast Graph Representation Learning with PyTorch Geometric](https://md.research.elamur.ai/free/en/papers/fey-2019-fast-graph-representation-learning-pytorch/)
- [Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations](https://md.research.elamur.ai/free/en/papers/lee-2020-weakly-supervised-modeling-contextualized-event/)
- [Graph Attention Networks](https://md.research.elamur.ai/free/en/papers/veli-2017-vckovic-graph-attention-networks/)
- [A Comprehensive Survey on Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
- [How Powerful are Graph Neural Networks?](https://md.research.elamur.ai/free/en/papers/xu-2018-powerful-graph-neural-networks/)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://md.research.elamur.ai/free/en/papers/xu-2020-distinguish-confusing-law-articles-legal/)
- [大模型驱动的文本属性图学习研究进展](https://md.research.elamur.ai/free/en/papers/zheng-2026-recent-advances-text/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{hamilton2017inductive,
  title = {{Inductive Representation Learning on Large Graphs}},
  author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure},
  year = {2017},
  doi = {10.48550/arxiv.1706.02216},
  eprint = {1706.02216},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1706.02216}
}
```

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