Review for NeurIPS paper: Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

Summary and Contributions: In this paper, the author presented a new graph learning method for graph neural networks. The authors started to analyze the significant drawbacks of existing GNNs methods: 1. work only when the graph data input is given; 2. ignore potentially imperfect graph inputs (due to the noise and cannot reflect true graph topology); 3. completely fail when inputs like texts are not given in graph format. To solve these problems, this paper proposed a new deep graph learning framework for learning the graph embedding and graph structure at the same time. Specifically, this paper introduced an iterative deep graph learning approach, where the key idea is to alternatively produce a better and more robust graph node embedding with a better learned graph structure and then learn a better graph structure based on better graph node embeddings. They further proposed a scalable version of the proposed method IDGL by leveraging the anchor-based approximation method. Graph similarity learning and graph regularization are also proposed to learn a graph structure with controlled quality, instead of learning a fully connected graph in existing methods.