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

We study a new research problem named semi-supervised few-shot multi-label node classification which has the following characteristics: 1) the extreme imbal-ance 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 out- performs the state-of-the-art methods with significant margins, especially when only a few-shot labeled nodes are available.