In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and leverage an adapted graph regularization for controlling smoothness, connectivity and sparsity of the generated graph. We further propose a novel iterative method for searching for a hidden graph structure that augments the initial graph structure. Our iterative method dynamically stops when the learned graph structure approaches close enough to the optimal graph. Our extensive experiments demonstrate that the proposed DIAL-GNN model can consistently outperform or match state-of-the-art baselines in terms of both downstream task performance and computational time. The proposed approach can cope with both transductive learning and inductive learning.

本篇研究论文提出了一种端到端图学习框架——DIAL-GNN，它能够同时学习图结构和图嵌入，并借助于一种自适应图正则化方法和一种迭代算法来优化图结构和性能，实验表明该方法在转导性和归纳性学习方面取得了不错的表现。

图神经网络的深度迭代自适应学习