Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification
The MLSGNN framework learns optimal graph structures for semi-supervised classification by combining semantic graph learning and multi-measure attention. It encodes complex semantic interactions into a semantic graph to preserve global structure, while adaptively fusing multiple similarity measures to optimize local structure. This approach outperforms baselines across six real-world datasets.