论文标题
用于基于图的半监督节点分类的密度感知的超图神经网络
Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification
论文作者
论文摘要
基于图形的半监督学习可以利用标记和未标记数据之间的连接关系，在许多人工智能应用中都表现出色。基于图的半监督节点分类的最具挑战性问题之一是如何在各种数据之间使用隐式信息来改善分类的性能。基于图的半监督学习的传统研究集中在数据之间的成对连接上。但是，实际应用程序中的数据相关性可能超出了成对的范围，并且更复杂。密度信息已被证明是一个重要的线索，但是在现有基于图的半监督节点分类方法中很少探索它。为了为基于图的半监督节点分类开发灵活而有效的模型，我们提出了一种新型的密度感知的超图神经网络（DA-HGNN）。在我们提出的方法中，提供了Hyper-Graph来探索数据之间的高阶语义相关性，并提出了一个密度感知的HyperGraph注意网络以探索高阶连接关系。在各种基准数据集中进行了广泛的实验，结果证明了该方法的有效性。
Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications. One of the most challenging problems for graph-based semi-supervised node classification is how to use the implicit information among various data to improve the performance of classifying. Traditional studies on graph-based semi-supervised learning have focused on the pairwise connections among data. However, the data correlation in real applications could be beyond pairwise and more complicated. The density information has been demonstrated to be an important clue, but it is rarely explored in depth among existing graph-based semi-supervised node classification methods. To develop a flexible and effective model for graph-based semi-supervised node classification, we propose a novel Density-Aware Hyper-Graph Neural Networks (DA-HGNN). In our proposed approach, hyper-graph is provided to explore the high-order semantic correlation among data, and a density-aware hyper-graph attention network is presented to explore the high-order connection relationship. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.
