摘要
In graph few-shot learning, meta-training tasks are sampled to improve the model's ability to learn from limited nodes. Existing methods adapted from computer vision, generally employ random task sampling, which can lead to excessive task randomness. This hinders effective training on the graph as models struggle to adapt to tasks with substantial variations in classes and nodes. To address this issue, we propose a novel method called TRARM, i.e., Task RAndomness Reduced graph Meta-learning to mitigate adverse effects of excessive task randomness. Firstly, we design progressive grouping-based sampling to adjust combinations of classes and nodes by stages, thereby enabling more focused and efficient meta-training. Secondly, complementing sampling, a unified memory-based meta-update module is first deployed to effectively accumulate cross-task knowledge, improving both efficiency and stability of meta-learning. Despite its simplicity, comprehensive experiments demonstrate the superior performance of TRARM on four widely used benchmarks.
