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.
翻译:为了有效地分类图表实例,图形神经网络需要有能力捕捉图中存在的全方位关系。一个胶囊是代表实体复杂特性的一组神经元,表明其在传统的进化神经网络中的优势。本文提议采用EM 路由机制(CapsGNNEM)生成高质量的图形嵌入器。一些真实世界图形数据集的实验结果表明,拟议的CapsGNNEM在图形分类任务中超过了9个最先进的模型。