图上类别均衡与强化主动学习
Class-Balanced and Reinforced Active Learning on Graphs
AI总结：
本文提出类别均衡强化主动学习框架GCBR及其升级版GCBR++，通过设计类别均衡感知状态和奖励函数并引入惩罚机制，解决GNN主动学习中类别不均衡问题，在多个数据集上取得优于现有基线的性能。
摘要：
图神经网络（GNNs）在节点分类、链接预测和图分类等多种应用中已展现出显著成功。针对GNNs的主动学习旨在从未标注数据中查询有价值的样本进行标注，从而以更低成本最大化GNNs的性能。然而，现有大多数用于GNNs的强化主动学习算法可能导致类别分布高度不均衡，尤其是在类别高度倾斜的场景下。使用类别不均衡标注数据训练的GNNs容易偏向多数类，而少数类的较低性能可能导致整体性能下降。为解决这一问题，我们提出了一种新颖的面向GNNs的类别均衡强化主动学习框架，名为GCBR。它学习一个最优策略来获取类别均衡且信息量大的节点进行标注，从而最大化使用所选标注节点训练的GNNs的性能。GCBR设计了类别均衡感知的状态，以及一个在模型性能和类别均衡之间实现权衡的奖励函数。采用强化学习算法Advantage Actor-Critic（A2C）来稳定且高效地学习最优策略。我们进一步通过引入惩罚机制将GCBR升级为GCBR++，以获得类别更加均衡的标注集。在多个数据集上的大量实验证明了所提方法的有效性，取得了优于最先进基线的卓越性能。
英文摘要：
Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a lower cost. However, most existing algorithms for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. GNNs trained with class-imbalanced labeled data are susceptible to bias toward majority classes, and the lower performance of minority classes may lead to a decline in overall performance. To tackle this issue, we propose a novel class-balanced and reinforced active learning framework for GNNs, namely, GCBR. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GCBR designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. The reinforcement learning algorithm Advantage Actor-Critic (A2C) is employed to learn an optimal policy stably and efficiently. We further upgrade GCBR to GCBR++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
