1 research outputs found
Graph Policy Network for Transferable Active Learning on Graphs
Graph neural networks (GNNs) have been attracting increasing popularity due
to their simplicity and effectiveness in a variety of fields. However, a large
number of labeled data is generally required to train these networks, which
could be very expensive to obtain in some domains. In this paper, we study
active learning for GNNs, i.e., how to efficiently label the nodes on a graph
to reduce the annotation cost of training GNNs. We formulate the problem as a
sequential decision process on graphs and train a GNN-based policy network with
reinforcement learning to learn the optimal query strategy. By jointly
optimizing over several source graphs with full labels, we learn a transferable
active learning policy which can directly generalize to unlabeled target graphs
under a zero-shot transfer setting. Experimental results on multiple graphs
from different domains prove the effectiveness of our proposed approach in both
settings of transferring between graphs in the same domain and across different
domains