We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new graph after a small amount of training. We show that current link prediction methods are generally ill-equipped to handle this task. They cannot effectively transfer learned knowledge from one graph to another and are unable to effectively learn from sparse samples of edges. To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph. Our framework leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization. Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph can learn to quickly adapt to a new graph using only a small sample of true edges, enabling not only fast adaptation but also improved results at convergence.
翻译:我们考虑的是在图表上进行几小链接预测的任务。 目标是从图表上的分布中学习, 以便模型能够在经过少量培训后在新的图表中快速推断缺失的边缘。 我们显示, 目前的链接预测方法一般都不具备处理这项任务的能力。 它们无法有效地将从一个图中学到的知识转移到另一个图中, 也无法有效地从稀少的边缘样本中吸取知识。 为了应对这一挑战, 我们引入了新的基于梯度的元学习框架Meta- Graph。 我们的框架利用了更高级的梯度以及一个学习的图形签名功能, 有条件地生成一个图形神经网络初始化。 我们用一套新颖的、 少数点链接预测基准显示, Meta- Graph 能够学会快速适应新的图表, 仅使用少量真实边缘样本, 不仅能够快速适应, 还能在趋同时改进结果 。