# Advancing GraphSAGE with A Data-Driven Node Sampling

arXiv: https://arxiv.org/abs/1904.12935 · HTML: https://mlsanity.com/paper/1904.12935/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [A Biased Graph Neural Network Sampler with Near-Optimal Regret](https://mlsanity.com/paper/2103.01089/) (2021)
- [Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks](https://mlsanity.com/paper/2302.02922/) (2023)
- [Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks](https://mlsanity.com/paper/2405.17460/) (2024)
- [GraphConfRec: A Graph Neural Network-Based Conference Recommender System](https://mlsanity.com/paper/2106.12340/) (2021)
- [Artist Similarity with Graph Neural Networks](https://mlsanity.com/paper/2107.14541/) (2021)
- [Cooperative Causal GraphSAGE](https://mlsanity.com/paper/2505.14748/) (2025)

## Abstract (arXiv)

As an efficient and scalable graph neural network, GraphSAGE has enabled an inductive capability for inferring unseen nodes or graphs by aggregating subsampled local neighborhoods and by learning in a mini-batch gradient descent fashion. The neighborhood sampling used in GraphSAGE is effective in order to improve computing and memory efficiency when inferring a batch of target nodes with diverse degrees in parallel. Despite this advantage, the default uniform sampling suffers from high variance in training and inference, leading to sub-optimum accuracy. We propose a new data-driven sampling approach to reason about the real-valued importance of a neighborhood by a non-linear regressor, and to use the value as a criterion for subsampling neighborhoods. The regressor is learned using a value-based reinforcement learning. The implied importance for each combination of vertex and neighborhood is inductively extracted from the negative classification loss output of GraphSAGE. As a result, in an inductive node classification benchmark using three datasets, our method enhanced the baseline using the uniform sampling, outperforming recent variants of a graph neural network in accuracy.