Disentangled Active Learning on Graphs.

Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling, improving robustness and reducing redundancy in graph learning. However, they often overlook the complex entanglement of latent factors inherent in graph-structured data. This oversight can lead to a sampling process that fails to ensure diversity at a finer-grained level, thereby missing the opportunity to sample more valuable nodes. To this end, we propose a novel approach, Disentangled Active Learning on Graphs (DALG). In this work, we first design the Disenconv-AL layer to learn disentangled feature embedding, then construct the influence graph for each node and create a dedicated "memory list" to store the resultant influence weights. On this basis, our approach aims to make the model not excessively focus on a few latent factors during the sampling phase. Specifically, we prioritize addressing latent factors with the most significant impact on the sampled node in the previous round, thereby ensuring that current sampling can better focus on other latent factors. Compared with existing methodologies, our approach pioneers reach diversity from the latent factor that drives the formation of graph data at a finer-grained level, thereby enabling further improvements in the benefits delivered with a limited labeling budget. Extensive experiments across eight public datasets show that DALG surpasses state-of-the-art graph active learning methods, achieving an improvement of up to approximately 15% in both Micro-F1 and Macro-F1.