Graph is a fundamental mathematical structure in characterizing relations
between different objects and has been widely used on various learning tasks.
Most methods implicitly assume a given graph to be accurate and complete.
However, real data is inevitably noisy and sparse, which will lead to inferior
results. Despite the remarkable success of recent graph representation learning
methods, they inherently presume that the graph is homophilic, and largely
overlook heterophily, where most connected nodes are from different classes. In
this regard, we propose a novel robust graph structure learning method to
achieve a high-quality graph from heterophilic data for downstream tasks. We
first apply a high-pass filter to make each node more distinctive from its
neighbors by encoding structure information into the node features. Then, we
learn a robust graph with an adaptive norm characterizing different levels of
noise. Afterwards, we propose a novel regularizer to further refine the graph
structure. Clustering and semi-supervised classification experiments on
heterophilic graphs verify the effectiveness of our method.

我们提出一种新颖的鲁棒图结构学习方法，从异质数据中构建高质量的图，进而应用于下游任务。我们首先使用高通滤波器对每个节点进行编码，使其与邻居节点更具有区分度，然后学习一个具有自适应范数的鲁棒图，进一步利用新颖的正则化方法来改进图的结构。对异质图的聚类和半监督分类实验证明了我们方法的有效性。

异质性下的鲁棒图结构学习

Robust Graph Structure Learning under Heterophily

Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's
function and condition in a non-invasive manner. Undersampling of the $k$-space
is employed to reduce the scan duration, thus increasing patient comfort and
reducing the risk of motion artefacts, at the cost of reduced image quality. In
this challenge paper, we investigate the use of a convolutional recurrent
neural network (CRNN) architecture to exploit temporal correlations in
supervised cine cardiac MRI reconstruction. This is combined with a
single-image super-resolution refinement module to improve single coil
reconstruction by 4.4\% in structural similarity and 3.9\% in normalised mean
square error compared to a plain CRNN implementation. We deploy a high-pass
filter to our $\ell_1$ loss to allow greater emphasis on high-frequency details
which are missing in the original data. The proposed model demonstrates
considerable enhancements compared to the baseline case and holds promising
potential for further improving cardiac MRI reconstruction.

研究提出了一种卷积递归神经网络（CRNN）架构，用于利用时间相关性在监督影像的心脏磁共振成像重建中。该模型结合了单图像超分辨率细化模块，相比于普通 CRNN 的实现，其在结构相似性和标准化均方误差方面，使单线圈重建提高了 4.4％和 3.9％。通过在 l1 损失中使用高通滤波器，可以更好地强调原始数据中缺失的高频细节，从而证明了该模型相较于基准案例的显著改进，并具有进一步提高心脏磁共振成像重建的潜力。