论文标题
当对比学习符合主动学习时：一个新颖的图表主动学习范式与自学
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
论文作者
论文摘要
本文研究了图形上的主动学习（AL），其目的是发现最有用的节点，以最大程度地提高图形神经网络（GNN）的性能。以前，大多数图形方法都专注于从精心选择的标记数据集中学习节点表示，并且忽略了大量未标记的数据。受对比学习的成功（CL）的动机，我们提出了一种新颖的范式，该范式将图与Cl无缝整合。虽然能够以自我监督的方式利用丰富的未标记数据的力量，但AL选择的节点进一步提供了语义信息，可以更好地指导表示表示。此外，以前的工作还可以在不考虑GNN的邻里传播方案的情况下测量节点的信息性，因此可以选择嘈杂的节点。我们认为，由于GNN的平滑性质，同粒子子图的中心节点应大大受益于模型训练。为此，我们提出了一个微型选择方案，该方案明确利用邻里信息并发现同粒子子图以促进主动选择。在五个公共数据集上进行的全面，无混杂的实验证明了我们方法比最先进的方法的优越性。
This paper studies active learning (AL) on graphs, whose purpose is to discover the most informative nodes to maximize the performance of graph neural networks (GNNs). Previously, most graph AL methods focus on learning node representations from a carefully selected labeled dataset with large amount of unlabeled data neglected. Motivated by the success of contrastive learning (CL), we propose a novel paradigm that seamlessly integrates graph AL with CL. While being able to leverage the power of abundant unlabeled data in a self-supervised manner, nodes selected by AL further provide semantic information that can better guide representation learning. Besides, previous work measures the informativeness of nodes without considering the neighborhood propagation scheme of GNNs, so that noisy nodes may be selected. We argue that due to the smoothing nature of GNNs, the central nodes from homophilous subgraphs should benefit the model training most. To this end, we present a minimax selection scheme that explicitly harnesses neighborhood information and discover homophilous subgraphs to facilitate active selection. Comprehensive, confounding-free experiments on five public datasets demonstrate the superiority of our method over state-of-the-arts.
