Semantic guide for semi-supervised few-shot multi-label node classification

Abstract

We study a new research problem named semi-supervised few-shot multi-label node classification which has the following characteristics: 1) the extreme imbalance between the number of labeled and unlabeled nodes that are connected on graphs (handled by semi-supervised node learning); 2) the few labeled nodes per label (few-shot learning); and 3) the semantical correlations among labels for they share the same subsets of nodes (multi-label classification). In this paper, we propose a Label-Aware Representation Network (LARN) model to tackle this problem, by taking advantage of the semantic knowledge of labels to characterize nodes and their neighbors. Such a label-aware feature learning process allows a node to prepare its representation by knowing how it will be classified. The learned rich representations so can combat the scarcity of labeled training nodes. A label correlation scanner is then proposed to adaptively capture the label correlation and extract the useful information to generate the final node representation. Experimental results demonstrate that LARN consistently outperforms the state-of-the-art methods with significant margins, especially when only a few-shot labeled nodes are available.