  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.
