Capsule Graph Neural Networks with EM Routing
This addresses graph classification for researchers and practitioners, offering a novel method that improves performance over existing approaches.
The paper tackled the problem of graph classification by proposing Capsule Graph Neural Networks with EM routing to capture part-whole relationships, resulting in outperforming nine state-of-the-art models on real-world datasets.
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.