Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation
[edit]
Proceedings of Machine Learning Research, PMLR 89:29462955, 2019.
Abstract
This paper is concerned with the problem of learning the mapping from one graph to another graph. Primarily, we focus on the issue of how to effectively learn the topology of the source graph and then decode it to form the topology of the target graph. We embed the topology of the graph into the states of nodes by exerting a topology constraint, which results in our TopologyFlow encoder. To decoder the encoded topology, we design a conditioned graph generation model with two edge generation options, which result in the EdgeBernoulli decoder and the EdgeConnect decoder. Experimental results on the 10nodes simple graph dataset illustrate the substantial progress of the proposed method. The MNIST digits skeleton mapping experiment also reveals the ability of our approach to discover different typologies.
Related Material


