Deep Iterative and Adaptive Learning for Graph Neural Networks

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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.