  To effectively classify graph instances, graph neural networks need to have
the capability to capture the part-whole relationship existing in a graph. A
capsule is a group of neurons representing complicated properties of entities,
which has shown its advantages in traditional convolutional neural networks.
This paper proposed novel Capsule Graph Neural Networks that use the EM routing
mechanism (CapsGNNEM) to generate high-quality graph embeddings. Experimental
results on a number of real-world graph datasets demonstrate that the proposed
CapsGNNEM outperforms nine state-of-the-art models in graph classification
tasks.
