Directed hypergraph neural network
This addresses node classification for directed hypergraphs, which is an incremental advancement over existing undirected graph methods.
The paper tackles node classification on irregular data structures by developing a novel directed hypergraph neural network method, achieving the highest accuracies compared to classic directed graph and directed hypergraph semi-supervised learning methods on Cora and Citeseer datasets.
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.