no code implementations • 10 Mar 2024 • Pengfei Ding, Yan Wang, Guanfeng Liu
In this paper, we provide a comprehensive review of existing FLHG methods, covering challenges, research progress, and future prospects.
no code implementations • 7 Jan 2024 • Pengfei Ding, Yan Wang, Guanfeng Liu, Nan Wang, Xiaofang Zhou
To address this challenging problem, we propose a novel Causal OOD Heterogeneous graph Few-shot learning model, namely COHF.
no code implementations • 10 Aug 2023 • Pengfei Ding, Yan Wang, Guanfeng Liu
In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and edges.
no code implementations • 11 Jul 2022 • Pengfei Ding, Yan Wang, Guanfeng Liu, Xiaofang Zhou
In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data.