GraphMDN: Leveraging graph structure and deep learning to solve inverse problems

Understand

The recent introduction of Graph Neural Networks (GNNs) and their growing popularity in the past few years has enabled the application of deep learning algorithms to non-Euclidean, graph-structured data.

• GNNs have achieved state-of-the-art results across an impressive array of graph-based machine learning problems.
• Nevertheless, despite their rapid pace of development, much of the work on GNNs has focused on graph classification and embedding techniques, largely ignoring regression tasks over graph data.
• In this paper, we develop a Graph Mixture Density Network (GraphMDN), which combines graph neural networks with mixture density network (MDN) outputs.