Graph diffusion models have emerged as state-of-the-art techniques in graph
generation, yet integrating domain knowledge into these models remains
challenging. Domain knowledge is particularly important in real-world
scenarios, where invalid generated graphs hinder deployment in practical
applications. Unconstrained and conditioned graph generative models fail to
guarantee such domain-specific structural properties. We present ConStruct, a
novel framework that allows for hard-constraining graph diffusion models to
incorporate specific properties, such as planarity or acyclicity. Our approach
ensures that the sampled graphs remain within the domain of graphs that verify
the specified property throughout the entire trajectory in both the forward and
reverse processes. This is achieved by introducing a specific edge-absorbing
noise model and a new projector operator. ConStruct demonstrates versatility
across several structural and edge-deletion invariant constraints and achieves
state-of-the-art performance for both synthetic benchmarks and attributed
real-world datasets. For example, by leveraging planarity in digital pathology
graph datasets, the proposed method outperforms existing baselines and enhances
generated data validity by up to 71.1 percentage points.

通过引入特定的边吸收噪声模型和新的投影算子，ConStruct 是一种允许硬约束图扩散模型以包含特定属性（如平面性或非循环性）的新框架。它在合成基准和属性现实数据集中展示了多功能性和最先进的性能，例如在数字病理学图数据集中，利用平面性的提出方法提高了生成数据的有效性高达 71.1 个百分点。

结构约束图的生成建模

Generative Modelling of Structurally Constrained Graphs

We present a novel machine-learning approach for detecting faint point
sources in high-contrast adaptive optics imaging datasets. The most widely used
algorithms for primary subtraction aim to decouple bright stellar speckle noise
from planetary signatures by subtracting an approximation of the temporally
evolving stellar noise from each frame in an imaging sequence. Our approach
aims to improve the stellar noise approximation and increase the planet
detection sensitivity by leveraging deep learning in a novel direct imaging
post-processing algorithm. We show that a convolutional autoencoder neural
network, trained on an extensive reference library of real imaging sequences,
accurately reconstructs the stellar speckle noise at the location of a
potential planet signal. This tool is used in a post-processing algorithm we
call Direct Exoplanet Detection with Convolutional Image Reconstruction, or
ConStruct. The reliability and sensitivity of ConStruct are assessed using real
Keck/NIRC2 angular differential imaging datasets. Of the 30 unique point
sources we examine, ConStruct yields a higher S/N than traditional PCA-based
processing for 67$\%$ of the cases and improves the relative contrast by up to
a factor of 2.6. This work demonstrates the value and potential of deep
learning to take advantage of a diverse reference library of point spread
function realizations to improve direct imaging post-processing. ConStruct and
its future improvements may be particularly useful as tools for post-processing
high-contrast images from the James Webb Space Telescope and extreme adaptive
optics instruments, both for the current generation and those being designed
for the upcoming 30 meter-class telescopes.

我们提出了一种新颖的机器学习方法，用于检测高对比度自适应光学成像数据中的微弱点源。通过利用深度学习在一种新颖的直接成像后处理算法中，我们的方法旨在改进恒星噪声的近似并提高行星探测灵敏度。我们展示了一个卷积自编码神经网络在广泛的实际成像序列参考库上进行训练，能够准确重构潜在行星信号位置的恒星斑点噪声。我们称这个工具为 ConStruct，通过使用真实的 Keck/NIRC2 角差分成像数据集评估了 ConStruct 的可靠性和灵敏度。在我们考察的 30 个独特的点源中，ConStruct 对 67％的情况产生比传统基于 PCA 的处理更高的信噪比，并且相对对比度提高了最多 2.6 倍。这项工作展示了深度学习利用多样的点扩散函数实现来改进直接成像后处理的价值和潜力。ConStruct 及其未来的改进可能特别适用于处理来自詹姆斯・韦伯太空望远镜和极端自适应光学仪器的高对比度图像，无论是对于当前的望远镜还是设计中的 30 米级望远镜。