Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning
Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning
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DOI：
10.1609/aaai.v36i8.20897
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Yuheng Zhang;Hanghang Tong;Yinglong Xia;Yan Zhu-;Yuejie Chi;Lei Ying
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文献类型：
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作者：
Yuheng Zhang;Hanghang Tong;Yinglong Xia;Yan Zhu-;Yuejie Chi;Lei Ying
图神经网络（GNNs）在许多图学习任务中取得了巨大的成功，如节点分类，图分类和链接预测。对于分类任务，GNN的性能通常高度依赖于标记节点的数量，因此可能会由于昂贵的注释成本而受到严重阻碍。关于GNN主动学习的稀疏文献主要集中在每次迭代只选择一个样本，这对于大规模数据集来说效率低下。在本文中，我们研究了GNNs的批量主动学习设置，其中学习代理可以每次获取多个样本的标签。我们将批处理主动学习归结为一个协作多智能体强化学习问题，并提出了一个新的强化批处理模式主动学习框架BiGeNe。为了避免联合作用空间的组合爆炸，我们引入了一种值分解方法，将总Q值分解为单个Q值的平均值。此外，我们提出了一种新的多智能体Q-网络组成的图卷积网络（GCN）组件和门控递归单元（GRU）组件。GCN组件考虑节点之间的信息性和相互依赖性，GRU组件使代理能够考虑同一批中选定节点之间的交互。在多个公共数据集上的实验结果证明了该方法的有效性和效率。
Graph neural networks (GNNs) have achieved tremendous success in many graph learning tasks such as node classification, graph classification and link prediction. For the classification task, GNNs' performance often highly depends on the number of labeled nodes and thus could be significantly hampered due to the expensive annotation cost. The sparse literature on active learning for GNNs has primarily focused on selecting only one sample each iteration, which becomes inefficient for large scale datasets. In this paper, we study the batch active learning setting for GNNs where the learning agent can acquire labels of multiple samples at each time. We formulate batch active learning as a cooperative multi-agent reinforcement learning problem and present a novel reinforced batch-mode active learning framework BiGeNe. To avoid the combinatorial explosion of the joint action space, we introduce a value decomposition method that factorizes the total Q-value into the average of individual Q-values. Moreover, we propose a novel multi-agent Q-network consisting of a graph convolutional network (GCN) component and a gated recurrent unit (GRU) component. The GCN component takes both the informativeness and inter-dependences between nodes into account and the GRU component enables the agent to consider interactions between selected nodes in the same batch. Experimental results on multiple public datasets demonstrate the effectiveness and efficiency of our proposed method.