# Partition-Based Active Learning for Graph Neural Networks

arXiv: https://arxiv.org/abs/2201.09391 · HTML: https://mlsanity.com/paper/2201.09391/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks](https://mlsanity.com/paper/1707.09564/) (2017)
- [Contextual Stochastic Block Models](https://mlsanity.com/paper/1807.09596/) (2018)
- [Subgroup Generalization and Fairness of Graph Neural Networks](https://mlsanity.com/paper/2106.15535/) (2021)
- [Active Learning for Graph Embedding](https://mlsanity.com/paper/1705.05085/) (2017)
- [Graph Policy Network for Transferable Active Learning on Graphs](https://mlsanity.com/paper/2006.13463/) (2020)
- [Active Learning for Graph Neural Networks via Node Feature Propagation](https://mlsanity.com/paper/1910.07567/) (2019)
- [S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification](https://mlsanity.com/paper/1506.08760/) (2015)
- [Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach](https://mlsanity.com/paper/1711.05174/) (2017)
- [ActiveHNE: Active Heterogeneous Network Embedding](https://mlsanity.com/paper/1905.05659/) (2019)
- [SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs](https://mlsanity.com/paper/1908.08169/) (2019)
- [A Meta-Learning Approach to One-Step Active Learning](https://mlsanity.com/paper/1706.08334/) (2017)
- [Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation](https://mlsanity.com/paper/2007.05003/) (2020)

## Built on by (most cited first, 12 of 12)

- [Label-free Node Classification on Graphs with Large Language Models (LLMS)](https://mlsanity.com/paper/2310.04668/) (2023)
- [Model Generalization on Text Attribute Graphs: Principles with Large Language Models](https://mlsanity.com/paper/2502.11836/) (2025)
- [Dissimilar Nodes Improve Graph Active Learning](https://mlsanity.com/paper/2212.01968/) (2022)
- [Towards Label Position Bias in Graph Neural Networks](https://mlsanity.com/paper/2305.15822/) (2023)
- [On the Topology Awareness and Generalization Performance of Graph Neural Networks](https://mlsanity.com/paper/2403.04482/) (2024)
- [Robust Offline Active Learning on Graphs](https://mlsanity.com/paper/2408.07941/) (2024)
- [DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation](https://mlsanity.com/paper/2409.08946/) (2024)
- [Language Models as Messengers: Enhancing Message Passing in Heterophilic Graph Learning](https://mlsanity.com/paper/2505.19762/) (2025)
- [A Structural-Clustering Based Active Learning for Graph Neural Networks](https://mlsanity.com/paper/2312.04307/) (2023)
- [LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models](https://mlsanity.com/paper/2609.05955/) (2026)
- [GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments](https://mlsanity.com/paper/2506.10120/) (2025)
- [ALINC: Active Learning for Inductive Node Classification via Graph Sampling](https://mlsanity.com/paper/2606.04647/) (2026)

## Abstract (arXiv)

We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in an active learning setup. We propose GraphPart, a novel partition-based active learning approach for GNNs. GraphPart first splits the graph into disjoint partitions and then selects representative nodes within each partition to query. The proposed method is motivated by a novel analysis of the classification error under realistic smoothness assumptions over the graph and the node features. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method outperforms existing active learning methods for GNNs under a wide range of annotation budget constraints. In addition, the proposed method does not introduce additional hyperparameters, which is crucial for model training, especially in the active learning setting where a labeled validation set may not be available.