论文标题
定向超图神经网络
Directed hypergraph neural network
论文作者
论文摘要
为了处理不规则的数据结构，许多数据科学家已经开发了图形卷积神经网络。但是，数据科学家只是主要集中于开发未指导图的深神经网络方法。在本文中，我们将介绍用于定向超图的新型神经网络方法。换句话说，我们不仅将开发新型的定向超图神经网络方法，而且还将开发基于新颖的指导性超图半监督学习方法。这些方法用于解决节点分类任务。实验中使用的两个数据集是Cora和Citeseer数据集。在经典的基于图形的半监督学习方法中，基于新颖的基于HyperGraph的半监督学习方法，用于解决此节点分类任务的新型定向超图神经网络方法，我们认识到这种新颖的定向HyperGraph神经网络可实现最高精度。
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.
