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Chapter 12 Graph Neural Networks: Graph Transformation

Chapter 12 Graph Neural Networks: Graph TransformationXiaojie Guo, Shiyu Wang, Liang ZhaoAbstractMany problems regarding structured predictions are encountered in theprocess of transforming a Graph in the source domain into another Graph in targetdomain, which requires to learn a Transformation mapping from the source to targetdomains. For example, it is important to study how structural connectivity influencesfunctional connectivity in brain networks and traffic networks. It is also common tostudy how a protein ( , a network of atoms) folds, from its primary structureto tertiary structure. In this Chapter , we focus on the Transformation problem thatinvolves graphs in the domain of deep Graph Neural networks. First, the problemof Graph Transformation in the domain of Graph Neural networks are formalized inSection Considering the entities that are being transformed during the trans-formation process, the Graph Transformation problem is further divided into fourcategories, namely node-level Transformation , edge-level Transformation , node-edgeco- Transformation , as well as other Graph -involved transformations ( , sequence-to- Graph Transformation and context-to- Graph Transformation ), which are discussedin Section to Section , respectively.

involves graphs in the domain of deep graph neural networks. First, the problem of graph transformation in the domain of graph neural networks are formalized in Section 12.1. Considering the entities that are being transformed during the trans-formation process, the graph transformation problem is further divided into four

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