Graph Neural Networks: A Review of Methods and Applications

Understand

Lots of learning tasks require dealing with graph data which contains rich relation information among elements.

• Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs.
• In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models.
• Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs.