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

TL;DR

LSCALE introduces a novel active learning framework for node classification that leverages both labelled and unlabelled node representations through clustering, significantly improving performance over existing methods.

Contribution

The paper proposes a new latent space clustering-based active learning method that fully utilizes unlabelled node representations to enhance node classification performance.

Findings

Outperforms state-of-the-art methods on five datasets

Uses K-Medoids clustering on a combined feature space

Employs incremental clustering to reduce redundancy

Abstract

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.