GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing

Understand

In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks.

• However, current neural-message passing architectures typically need to perform an expensive recursive neighborhood expansion in multiple rounds and consequently suffer from a scalability issue.
• Moreover, most existing neural-message passing schemes are inflexible since they are restricted to fixed-hop neighborhoods and insensitive to the actual demands of different nodes.
• We circumvent these limitations by a novel feature-message passing framework, called Graph Multi-layer Perceptron (GMLP), which separates the neural update from the message passing.