  Graph Convolutional Neural Networks (GCNNs) are the most recent exciting
advancement in deep learning field and their applications are quickly spreading
in multi-cross-domains including bioinformatics, chemoinformatics, social
networks, natural language processing and computer vision. In this paper, we
expose and tackle some of the basic weaknesses of a GCNN model with a capsule
idea presented in \cite{hinton2011transforming} and propose our Graph Capsule
Network (GCAPS-CNN) model. In addition, we design our GCAPS-CNN model to solve
especially graph classification problem which current GCNN models find
challenging. Through extensive experiments, we show that our proposed Graph
Capsule Network can significantly outperforms both the existing state-of-art
deep learning methods and graph kernels on graph classification benchmark
datasets.
