  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.
