Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning


  • Yuheng Zhang University of Illinois at Urbana-Champaign
  • Hanghang Tong University of Illinois at Urbana-Champaign
  • Yinglong Xia Facebook AI
  • Yan Zhu Facebook AI
  • Yuejie Chi CMU
  • Lei Ying University of Michigan



Machine Learning (ML)


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.




How to Cite

Zhang, Y., Tong, H., Xia, Y., Zhu, Y., Chi, Y., & Ying, L. (2022). Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 9118-9126.



AAAI Technical Track on Machine Learning III