Active Heterogeneous Graph Neural Networks with Per-step Meta-Q-Learning
Active Heterogeneous Graph Neural Networks with Per-step Meta-Q-Learning
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DOI：
10.1109/icdm54844.2022.00176
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Yuheng Zhang;Yinglong Xia;Yan Zhu;Yuejie Chi;Lei Ying;H. Tong
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文献类型：
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作者：
Yuheng Zhang;Yinglong Xia;Yan Zhu;Yuejie Chi;Lei Ying;H. Tong
近年来，异构图神经网络在处理异构信息网络方面表现出了上级的性能。尽管如此，HGNN的成功通常取决于足够的标记训练数据的可用性，这在真实的场景中可能非常昂贵。主动学习为应对数据稀缺挑战提供了有效的解决方案。对于绝大多数现有的关于图上的主动学习的工作，他们主要集中在同构图上，因此在HIN上不适用甚至不适用。在本文中，我们研究了主动学习问题与HGNNs，并提出了一种新的元增强主动学习框架MetRA。以往的增强型主动学习算法在标记源图上训练策略网络，并直接将策略转移到目标图，而不进行任何调整。为了更好地利用自适应阶段目标图中的信息，我们提出了一种新的基于元Q学习的策略传输算法，称为每步MQL。对HIN的实证评估证明了我们提出的框架的有效性。在Micro-F1中，最佳基线的改进高达7%。
Recent years have witnessed the superior performance of heterogeneous graph neural networks (HGNNs) in dealing with heterogeneous information networks (HINs). Nonetheless, the success of HGNNs often depends on the availability of sufficient labeled training data, which can be very expensive to obtain in real scenarios. Active learning provides an effective solution to tackle the data scarcity challenge. For the vast majority of the existing work regarding active learning on graphs, they mainly focus on homogeneous graphs, and thus fall in short or even become inapplicable on HINs. In this paper, we study the active learning problem with HGNNs and propose a novel meta-reinforced active learning framework MetRA. Previous reinforced active learning algorithms train the policy network on labeled source graphs and directly transfer the policy to the target graph without any adaptation. To better exploit the information from the target graph in the adaptation phase, we propose a novel policy transfer algorithm based on meta-Q-learning termed per-step MQL. Empirical evaluations on HINs demonstrate the effectiveness of our proposed framework. The improvement over the best baseline is up to 7% in Micro-F1.