# Active Learning for Graph Neural Networks via Node Feature Propagation

arXiv: https://arxiv.org/abs/1910.07567 · HTML: https://mlsanity.com/paper/1910.07567/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 30 of 37)

- [Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs](https://mlsanity.com/paper/2307.03393/) (2023)
- [Label-free Node Classification on Graphs with Large Language Models (LLMS)](https://mlsanity.com/paper/2310.04668/) (2023)
- [CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://mlsanity.com/paper/2307.15770/) (2023)
- [Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization](https://mlsanity.com/paper/2108.00219/) (2021)
- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [Towards Data-centric Graph Machine Learning: Review and Outlook](https://mlsanity.com/paper/2309.10979/) (2023)
- [Partition-Based Active Learning for Graph Neural Networks](https://mlsanity.com/paper/2201.09391/) (2022)
- [Uncertainty for Active Learning on Graphs](https://mlsanity.com/paper/2405.01462/) (2024)
- [LEGO-Learn: Label-Efficient Graph Open-Set Learning](https://mlsanity.com/paper/2410.16386/) (2024)
- [Interscript: A dataset for interactive learning of scripts through error feedback](https://mlsanity.com/paper/2112.07867/) (2021)
- [FastGAS: Fast Graph-based Annotation Selection for In-Context Learning](https://mlsanity.com/paper/2406.03730/) (2024)
- [Multitask Active Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2401.13210/) (2024)
- [CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2112.05975/) (2021)
- [Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs](https://mlsanity.com/paper/2412.11983/) (2024)
- [Few-Shot Graph Out-of-Distribution Detection with LLMs](https://mlsanity.com/paper/2503.22097/) (2025)
- [Improving Neural Model Performance through Natural Language Feedback on Their Explanations](https://mlsanity.com/paper/2104.08765/) (2021)
- [A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction](https://mlsanity.com/paper/2304.13032/) (2023)
- [SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction](https://mlsanity.com/paper/2212.01440/) (2022)
- [Dissimilar Nodes Improve Graph Active Learning](https://mlsanity.com/paper/2212.01968/) (2022)
- [Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning](https://mlsanity.com/paper/2308.08823/) (2023)
- [On the Topology Awareness and Generalization Performance of Graph Neural Networks](https://mlsanity.com/paper/2403.04482/) (2024)
- [Uplift Modeling Under Limited Supervision](https://mlsanity.com/paper/2403.19289/) (2024)
- [Test-Time Training on Graphs with Large Language Models (LLMs)](https://mlsanity.com/paper/2404.13571/) (2024)
- [Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models](https://mlsanity.com/paper/2407.13989/) (2024)
- [Tackling Provably Hard Representative Selection via Graph Neural Networks](https://mlsanity.com/paper/2205.10403/) (2022)
- [ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning](https://mlsanity.com/paper/2206.04255/) (2022)
- [DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification](https://mlsanity.com/paper/2308.00146/) (2023)
- [CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning](https://mlsanity.com/paper/2311.11073/) (2023)
- [A Structural-Clustering Based Active Learning for Graph Neural Networks](https://mlsanity.com/paper/2312.04307/) (2023)
- [Active Learning for Graphs with Noisy Structures](https://mlsanity.com/paper/2402.02321/) (2024)

## Abstract (arXiv)

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data. However, a large quantity of labeled graphs is difficult to obtain, which significantly limits the true success of GNNs. Although active learning has been widely studied for addressing label-sparse issues with other data types like text, images, etc., how to make it effective over graphs is an open question for research. In this paper, we present an investigation on active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node feature propagation followed by K-Medoids clustering of the nodes for instance selection in active learning. With a theoretical bound analysis we justify the design choice of our approach. In our experiments on four benchmark datasets, the proposed method outperforms other representative baseline methods consistently and significantly.