论文标题
多视图图结构学习使用在格拉曼歧管上合并的子空间
Multi-view graph structure learning using subspace merging on Grassmann manifold
论文作者
论文摘要
最近已经开发了许多成功的学习算法来表示图形结构化数据。例如，图形神经网络（GNN）在各种任务中取得了巨大的成功，例如节点分类，图形分类和链接预测。但是，这些方法高度取决于输入图结构的质量。一种减轻此问题的使用方法是学习图形结构，而不是依靠手动设计的图。在本文中，我们使用多视图学习介绍了一种新的图形结构学习方法，称为MV-GSL（多视图图结构学习），其中我们使用在Grassmann流形上合并的子空间来汇总不同的图结构学习方法，以提高学习图结构的质量。进行了广泛的实验，以评估在两个基准数据集Cora和Citeseer上提出的方法的有效性。我们的实验表明，与单个图形结构学习方法相比，所提出的方法具有有希望的性能。
Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classification, graph classification, and link prediction. However, these methods are highly dependent on the quality of the input graph structure. One used approach to alleviate this problem is to learn the graph structure instead of relying on a manually designed graph. In this paper, we introduce a new graph structure learning approach using multi-view learning, named MV-GSL (Multi-View Graph Structure Learning), in which we aggregate different graph structure learning methods using subspace merging on Grassmann manifold to improve the quality of the learned graph structures. Extensive experiments are performed to evaluate the effectiveness of the proposed method on two benchmark datasets, Cora and Citeseer. Our experiments show that the proposed method has promising performance compared to single and other combined graph structure learning methods.
