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。

利用图传播模型进行网络细化任务

Leveraging Graph Diffusion Models for Network Refinement Tasks

Knowledge graphs have been adopted in many diverse fields for a variety of
purposes. Most of those applications rely on valid and complete data to deliver
their results, pressing the need to improve the quality of knowledge graphs. A
number of solutions have been proposed to that end, ranging from rule-based
approaches to the use of probabilistic methods, but there is an element that
has not been considered yet: the edit history of the graph. In the case of
collaborative knowledge graphs (e.g., Wikidata), those edits represent the
process in which the community reaches some kind of fuzzy and distributed
consensus over the information that best represents each entity, and can hold
potentially interesting information to be used by knowledge graph refinement
methods. In this paper, we explore the use of edit history information from
Wikidata to improve the performance of type prediction methods. To do that, we
have first built a JSON dataset containing the edit history of every instance
from the 100 most important classes in Wikidata. This edit history information
is then explored and analyzed, with a focus on its potential applicability in
knowledge graph refinement tasks. Finally, we propose and evaluate two new
methods to leverage this edit history information in knowledge graph embedding
models for type prediction tasks. Our results show an improvement in one of the
proposed methods against current approaches, showing the potential of using
edit information in knowledge graph refinement tasks and opening new promising
research lines within the field.

本文探讨使用维基数据图的编辑历史信息来提高类型预测方法的性能，设计并评估了两种新方法来利用此信息进行知识图嵌入模型，结果表明这种方法在知识图改进任务中有潜力，并在该领域内开辟了新的有前途的研究方向。