Interaction networks can be trained to reason about the interactions of objects in a complex physical system. It can make predictions and inferences about various system properties in domains such as collision dynamics (rigid and non-rigid). It simulates these systems using object and relation centric reasonings using deep neural networks on graphs.
The nano-scale molecules have an inherent graph like structure with the ions or the atoms being the nodes and the bonds between them, edges. GraphML can be applied in both scenarios: learning about existing molecular structures as well as discovering new chemical structures. This has had a significant impact in computer aided drug design.
Zero-shot image recognition relies on the existence of a labelled training set of seen classes and the knowledge about how each unseen class is semantically related to the seen ones. One approach is to leverage structural information, in the form of graphs. Knowledge graphs can provide the necessary information to guide this task.
Reading comprehension (and language understanding in general) is one of the complex reasoning tasks performed by humans; the answer to a question may not be located in any single part of the extract but may need global inferencing. Representing the passage in the form of a graph using a neural networks helps in better connecting the global evidences.
Combinatorial optimization problems over graphs are a set of NP-hard problems. Some of these can be solved by heuristic methods. In recent times, attempt is being made to solve them using deep neural networks. Consequently, GraphML techniques are also being leveraged to operate over these graph structured datasets.