Recurrent Attention Walk for Semi-supervised Classification
This work addresses semi-supervised node classification for graph-based learning, offering a flexible approach for both transductive and inductive settings, though it is incremental as it builds on existing graph convolution and attention mechanisms.
The paper tackles the problem of node classification in attributed networks by proposing a reinforcement learning-based method that explores neighborhoods via a walk path to improve classification accuracy, demonstrating superior performance over state-of-the-art methods in experiments on four datasets.
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.