Information Gain Propagation: a new way to Graph Active Learning with Soft Labels

TL;DR

This paper introduces a novel graph active learning approach using soft labels and relaxed queries, reducing labeling costs while maintaining high accuracy in GNNs.

Contribution

It proposes a new soft-label active learning method with relaxed queries and information gain propagation for GNNs, improving efficiency and performance.

Findings

Outperforms state-of-the-art GNN active learning methods

Reduces labeling costs significantly

Achieves higher accuracy on public datasets

Abstract

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learning (AL) methods are proposed to improve the labeling efficiency by selecting the most valuable nodes to label. Existing methods assume an oracle can correctly categorize all the selected nodes and thus just focus on the node selection. However, such an exact labeling task is costly, especially when the categorization is out of the domain of individual expert (oracle). The paper goes further, presenting a soft-label approach to AL on GNNs. Our key innovations are: i) relaxed queries where a domain expert (oracle) only judges the correctness of the predicted labels (a binary question) rather than identifying the exact class (a multi-class question), and ii) new criteria of maximizing information gain propagation for active learner with relaxed queries and soft labels. Empirical studies on public datasets demonstrate that our method significantly outperforms the state-of-the-art GNN-based AL methods in terms of both accuracy and labeling cost.