Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning
This work addresses the challenge of building deeper graph neural networks for inductive graph representation learning, which is important for researchers and practitioners in graph-based machine learning.
The paper tackles the problem of node representation learning in graphs by introducing recurrent graph neural networks (RGNN) to capture long-term dependencies across layers, achieving state-of-the-art results on Pubmed, Reddit, and PPI datasets.
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.