Recurrent Attention Walk for Semi-supervised Classification
Proceedings of the 13th International Conference on Web Search and Data Mining
In this paper, we study the graph-based semi-supervised learning for classifying nodes in a ributed networks, where the nodes and edges possess content information. Recent approaches like graph convolution networks and a ention mechanisms have been proposed to ensemble the rst-order neighbors and incorporate the relevant neighbors. However, it is costly (especially in memory) to consider all neighbors without a prior di erentiation. We propose to explore the neighborhood in a reinforcement
... reinforcement learning se ing and nd a walk path well-tuned for classifying the unlabelled target nodes. We let an agent (of node classi cation task) walk over the graph and decide where to direct to maximize classi cation accuracy. We de ne the graph walk as a partially observable Markov decision process (POMDP). e proposed method is exible for working in both transductive and inductive se ing. 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.