Routing methods in capsule networks often learn a hierarchical relationship for capsules in successive layers, but the intra-relation between capsules in the same layer is less studied, while this intra-relation is a key factor for the semantic understanding in text data. Therefore, in this paper, we introduce a new capsule network with graph routing to learn both relationships, where capsules in each layer are treated as the nodes of a graph. We investigate strategies to yield adjacency and degree matrix with three different distances from a layer of capsules, and propose the graph routing mechanism between those capsules. We validate our approach on five text classification datasets, and our findings suggest that the approach combining bottom-up routing and top-down attention performs the best. Such an approach demonstrates generalization capability across datasets. Compared to the state-of-the-art routing methods, the improvements in accuracy in the five datasets we used were 0.82, 0.39, 0.07, 1.01, and 0.02, respectively.

翻译：胶囊网络的运行方法往往在连续一层中学习胶囊的等级关系,但同一层中胶囊之间的内部关系研究较少,而这种内部关系是文本数据中语义理解的一个关键因素。因此,在本文件中,我们引入了新的胶囊网络,配有图形路径,以学习两种关系,将每个层中的胶囊作为图的节点处理。我们研究各种策略,以产生与胶囊层相距三个不同距离的相近和程度矩阵,并提出这些胶囊之间的图表路由机制。我们验证了我们关于五个文本分类数据集的方法,我们的调查结果表明,将底部上线和上部下线相结合的方法是最佳的。这种方法显示了跨数据集的概括能力。与最先进的路由方法相比,我们使用的五套数据集的准确性分别是0.82、0.39、0.07、1.01和0.02。