Perturbation-Based Graph Active Learning for Semi-Supervised Belief Representation Learning

This paper addresses the problem of optimizing the allocation of labeling resources to enhance the performance of semi-supervised belief representation learning in social networks. The objective is to strategically identify valuable nodes in social media graphs that are worth labeling within a constrained budget to maximize downstream learning task performance. Despite progress in unsupervised and semi-supervised methods for belief and ideology representation learning on social networks, the scarcity of high-quality labeled social data continues to pose a significant challenge. Therefore, allocating labeling efforts judiciously becomes critical in scenarios with limited resources for labeling. This paper introduces a perturbation-based active learning strategy inspired by graph augmentation, PerbALGraph, which progressively selects nodes for labeling using an automatic estimator, thereby eliminating the need for human guidance. This estimator is based on the principle that nodes in the network that exhibit heightened sensitivity to changes in structural features are better candidates for labeling. We design the estimator to be model-agnostic and application-independent and to score candidates under a set of designed graph perturbations. Extensive experiments on six real-world social media datasets demonstrate the superior performance and robustness of our proposed method compared to existing active learning approaches.