Corpus ID: 232110397

Deep Graph Structure Learning for Robust Representations: A Survey

@article{Zhu2021DeepGS,
  title={Deep Graph Structure Learning for Robust Representations: A Survey},
  author={Yanqiao Zhu and Weizhi Xu and Jinghao Zhang and Qiang Liu and Shu Wu and Liang Wang},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.03036}
}
Graph Neural Networks (GNNs) are widely used for analyzing graph-structured data. Most GNN methods are highly sensitive to the quality of graph structures and usually require a perfect graph structure for learning informative embeddings. However, the pervasiveness of noise in graphs necessitates learning robust representations for real-world problems. To improve the robustness of GNN models, many studies have been proposed around the central concept of Graph Structure Learning (GSL), which aims… Expand

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