# LSCALE: Latent Space Clustering-Based Active Learning for Node Classification

arXiv: https://arxiv.org/abs/2012.07065 · HTML: https://mlsanity.com/paper/2012.07065/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [LEGO-Learn: Label-Efficient Graph Open-Set Learning](https://mlsanity.com/paper/2410.16386/) (2024)
- [Multitask Active Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2401.13210/) (2024)
- [Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning](https://mlsanity.com/paper/2308.08823/) (2023)
- [DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification](https://mlsanity.com/paper/2308.00146/) (2023)
- [Active Learning for Graphs with Noisy Structures](https://mlsanity.com/paper/2402.02321/) (2024)
- [Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation](https://mlsanity.com/paper/2503.18195/) (2025)
- [Semantic Parsing in Limited Resource Conditions](https://mlsanity.com/paper/2309.07429/) (2023)

## Abstract (arXiv)

Node classification on graphs is an important task in many practical domains. It usually requires labels for training, which can be difficult or expensive to obtain in practice. Given a budget for labelling, active learning aims to improve performance by carefully choosing which nodes to label. Previous graph active learning methods learn representations using labelled nodes and select some unlabelled nodes for label acquisition. However, they do not fully utilize the representation power present in unlabelled nodes. We argue that the representation power in unlabelled nodes can be useful for active learning and for further improving performance of active learning for node classification. In this paper, we propose a latent space clustering-based active learning framework for node classification (LSCALE), where we fully utilize the representation power in both labelled and unlabelled nodes. Specifically, to select nodes for labelling, our framework uses the K-Medoids clustering algorithm on a latent space based on a dynamic combination of both unsupervised features and supervised features. In addition, we design an incremental clustering module to avoid redundancy between nodes selected at different steps. Extensive experiments on five datasets show that our proposed framework LSCALE consistently and significantly outperforms the stateof-the-art approaches by a large margin.