Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects

arXiv:2403.13834v1 Announce Type: new
Abstract: Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both academia and industry because prevailing studies on heterogeneous graphs often suffer from label sparsity. FLHG aims to tackle the performance degradation in the face of limited annotated data and there have been numerous recent studies proposing various methods and applications. In this paper, we provide a comprehensive review of existing FLHG methods, covering challenges, research progress, and future prospects. Specifically, we first formalize …

abstract academia annotated data arxiv attention challenges cs.lg data face few-shot few-shot learning graphs industry performance progress prospects sparsity studies type