Multi-view graph structure learning using subspace merging on Grassmann manifold
This work addresses graph structure learning for machine learning applications, but it is incremental as it builds on existing multi-view and manifold techniques.
The paper tackles the problem of graph structure quality dependency in learning algorithms by introducing MV-GSL, a multi-view approach that aggregates different graph structure learning methods using subspace merging on Grassmann manifold, showing promising performance on Cora and Citeseer datasets.
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.