To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semi-supervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest accuracies.

翻译：为了处理不规则的数据结构,许多数据科学家开发了图形共振神经网络。然而,数据科学家只是主要集中于为非定向图形开发深神经网络方法。在本文中,我们将介绍用于定向高射线的新型神经网络方法。换句话说,我们将不仅开发新的定向高射线神经网络方法,而且开发新的定向高射线网络方法。这些方法用于解决节点分类任务。实验中使用的两套数据集是 Cora 和引文数据集。在经典定向图形半监督学习方法中,即新型定向高射线半监督学习方法,即用于解决这一节点分类任务的新型定向高射线网络方法,我们认识到新式定向高射线网络达到了最高值。