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.
CITATION STYLE
Lei, Y., & Zhang, J. (2021). Capsule Graph Neural Networks with em Routing. In International Conference on Information and Knowledge Management, Proceedings (pp. 3191–3195). Association for Computing Machinery. https://doi.org/10.1145/3459637.3482069
Mendeley helps you to discover research relevant for your work.