Conditional Graph Generation with Graph Principal Flow Network

Conditional graph generation is crucial and challenging since the conditional distribution of graph topology and feature is complicated and the semantic feature is hard to be captured by the generative model. In this work, we propose a novel graph conditional generative model, termed Graph Principal Flow Network (GPrinFlowNet), which enables us to progressively generate graphs from low- to high-frequency components. Our GPrinFlowNet effectively captures the subtle yet essential semantic features of graph topology, resulting in high-quality generated graph data.