For real-world graph data, the complex relationship between nodes is often represented as a hard binary link. Obviously, it is a discrete and simplified form of continuous relationship between nodes, which seriously limits the expressibility of the learned node representation. On the other hand, the node representation obtained in the embedding space can in turn be used to reveal the intrinsic relationship between nodes. To better characterize the node relationships and further facilitate the learning of node representation, an intuitive way is to refine the originally given graph structure with the embedded node representations. However, such global refinement of the relationships among all nodes without distinction will inevitably lead to some noisy edges, which may further confuse the training of the node representation learning model. In addition, it also has scalability problems on large graphs. To address these issues, we propose a local structure aware graph refinement to progressively reveal the latent relationships of nodes, thus achieving efficient and robust graph refinement.

翻译：对于现实世界的图数据，节点间的复杂关系通常被表示为硬性二元连接。显然，这是节点间连续关系的一种离散且简化的形式，严重限制了学习到的节点表示的表达能力。另一方面，在嵌入空间中获得的节点表示可以反过来用于揭示节点间的内在关系。为了更好地刻画节点关系并进一步促进节点表示的学习，一种直观的方式是利用嵌入的节点表示来优化原始给定的图结构。然而，不加区分地对所有节点间的关系进行这种全局优化，不可避免地会产生一些噪声边，从而可能干扰节点表示学习模型的训练。此外，这种方法在大规模图上还存在可扩展性问题。为了解决这些问题，我们提出了一种基于局部结构感知的图优化方法，逐步揭示节点的潜在关系，从而实现高效且鲁棒的图优化。