In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedding. The key rationale of IDGL is to learn a better graph structure based on better node embeddings, and vice versa (i.e., better node embeddings based on a better graph structure). Our iterative method dynamically stops when the learned graph approaches close enough to the graph optimized for the prediction task. In addition, we cast the graph learning problem as a similarity metric learning problem and leverage adaptive graph regularization for controlling the quality of the learned graph. Finally, combining the anchor-based approximation technique, we further propose a scalable version of IDGL, namely IDGL-ANCH, which significantly reduces the time and space complexity of IDGL without compromising the performance. Our extensive experiments on nine benchmarks show that our proposed IDGL models can consistently outperform or match state-of-the-art baselines. Furthermore, IDGL can be more robust to adversarial graphs and cope with both transductive and inductive learning.

本文提出了一种名为IDGL的端到端图学习框架，可以联合学习图结构和图嵌入，并将图学习问题转化为相似度度量学习问题，使用图正则化来控制学习图的质量，使用基于锚点的近似技术提高其可伸缩性。IDGL模型在九个基准测试中表现优异，并能够更好地应对恶意图和在归纳和转导学习中的应用。

基于迭代深层图神经网络的图嵌入算法：更好更稳健的节点嵌入