Most real-world networks are noisy and incomplete samples from an unknown
target distribution. Refining them by correcting corruptions or inferring
unobserved regions typically improves downstream performance. Inspired by the
impressive generative capabilities that have been used to correct corruptions
in images, and the similarities between "in-painting" and filling in missing
nodes and edges conditioned on the observed graph, we propose a novel graph
generative framework, SGDM, which is based on subgraph diffusion. Our framework
not only improves the scalability and fidelity of graph diffusion models, but
also leverages the reverse process to perform novel, conditional generation
tasks. In particular, through extensive empirical analysis and a set of novel
metrics, we demonstrate that our proposed model effectively supports the
following refinement tasks for partially observable networks: T1: denoising
extraneous subgraphs, T2: expanding existing subgraphs and T3: performing
"style" transfer by regenerating a particular subgraph to match the
characteristics of a different node or subgraph.

我们提出了一种基于子图扩散的新型图生成框架 SGDM，它不仅提高了图扩散模型的可扩展性和准确性，还通过逆过程执行了多种条件生成任务，包括去噪、扩展和样式迁移等。