  Graph Neural Networks (GNNs) have attracted increasing attention in recent
years and have achieved excellent performance in semi-supervised node
classification tasks. The success of most GNNs relies on one fundamental
assumption, i.e., the original graph structure data is available. However,
recent studies have shown that GNNs are vulnerable to the complex underlying
structure of the graph, making it necessary to learn comprehensive and robust
graph structures for downstream tasks, rather than relying only on the raw
graph structure. In light of this, we seek to learn optimal graph structures
for downstream tasks and propose a novel framework for semi-supervised
classification. Specifically, based on the structural context information of
graph and node representations, we encode the complex interactions in semantics
and generate semantic graphs to preserve the global structure. Moreover, we
develop a novel multi-measure attention layer to optimize the similarity rather
than prescribing it a priori, so that the similarity can be adaptively
evaluated by integrating measures. These graphs are fused and optimized
together with GNN towards semi-supervised classification objective. Extensive
experiments and ablation studies on six real-world datasets clearly demonstrate
the effectiveness of our proposed model and the contribution of each component.
