Graph neural networks have been proved very effective for a variety of prediction tasks on graphs such as node classification. Generally, a large number of labeled data are required to train these networks. However, in reality it could be very expensive to obtain a large number of labeled data on large-scale graphs. In this paper, we studied active learning for graph neural networks, i.e., how to effectively label the nodes on a graph for training graph neural networks. We formulated the problem as a sequential decision process, which sequentially label informative nodes, and trained a policy network to maximize the performance of graph neural networks for a specific task. Moreover, we also studied how to learn a universal policy for labeling nodes on graphs with multiple training graphs and then transfer the learned policy to unseen graphs. Experimental results on both settings of a single graph and multiple training graphs (transfer learning setting) prove the effectiveness of our proposed approaches over many competitive baselines.
Thank you for the author response.
Original review:
This paper presents a method for active learning on graphs, including a novel setting of transferring an active learning policy to unseen graphs.  The problems tackled here are important and the method is shown to improve over previous work in some cases.  On the down side, the evaluation may be missing one important method of comparison, the reasons for the proposed approach winning over previous work are not made explicit, and the empirical advantage of the approach is inconsistent (by my count, in the majority of cases the F1 advantage over previous work is within the standard error).  This paper has strengths but I feel it needs further refinement before publication.
The introduction of the paper claims that existing approaches for active learning on graphs are domain-specific and may not apply well to new domains.  But later, in the experiments, different reasons for the proposed approach's wins are given (in particular, on large graphs the proposed approach does relatively better vs. AGE which the paper suggests is due to the more complex nonlinear models used in the proposed approach).  In general, this paper’s approach tends to win over the primary baseline (the AGE method from Cai et al. 2017), but the wins are relatively small and inconsistent (esp. taking into account the standard error) whether the methods are evaluated in the single-graph setting or the transfer learning setting (of the homologous or heterogeneous variety).  If the limitation of previous work was domain-specificity, I would expect to see much larger wins on the transfer learning setting.  In general, an analysis that explains more what is driving the gains of this approach over the AGE approach would help us know how to build on this paper’s method in future work.
I was curious why the paper does not compare against the following work, which also presents an approach that wins over AGE:
“Active Discriminative Network Representation Learning,” Gao et al., IJCAI 2018
Lastly, the distillation-based approach, which learns graph-specific policies that are trained to fit their target graphs and to minimize their KL divergence from a single global shared policy, was interesting.  The fact that it doesn’t work much better than the joint policy is somewhat disappointing, but it’s still interesting.
Minor:
Sec 3.2: unclosed parenthesis in first paragraph
“Moreover, we also average the struc2vec features of all previously annotated nodes to capture the historical information” -- since the model has only node-level features, I didn’t understand how this average across multiple nodes was fed in as a node feature.  Is it used in all nodes?  Only annotated nodes?