  In this paper, we study the graph-based semi-supervised learning for
classifying nodes in attributed networks, where the nodes and edges possess
content information. Recent approaches like graph convolution networks and
attention mechanisms have been proposed to ensemble the first-order neighbors
and incorporate the relevant neighbors. However, it is costly (especially in
memory) to consider all neighbors without a prior differentiation. We propose
to explore the neighborhood in a reinforcement learning setting and find a walk
path well-tuned for classifying the unlabelled target nodes. We let an agent
(of node classification task) walk over the graph and decide where to direct to
maximize classification accuracy. We define the graph walk as a partially
observable Markov decision process (POMDP). The proposed method is flexible for
working in both transductive and inductive setting. Extensive experiments on
four datasets demonstrate that our proposed method outperforms several
state-of-the-art methods. Several case studies also illustrate the meaningful
movement trajectory made by the agent.
