  In this paper, we study the problem of node representation learning with
graph neural networks. We present a graph neural network class named recurrent
graph neural network (RGNN), that address the shortcomings of prior methods. By
using recurrent units to capture the long-term dependency across layers, our
methods can successfully identify important information during recursive
neighborhood expansion. In our experiments, we show that our model class
achieves state-of-the-art results on three benchmarks: the Pubmed, Reddit, and
PPI network datasets. Our in-depth analyses also demonstrate that incorporating
recurrent units is a simple yet effective method to prevent noisy information
in graphs, which enables a deeper graph neural network.
