Analyzing connections between brain regions of interest (ROI) is vital to
detect neurological disorders such as autism or schizophrenia. Recent
advancements employ graph neural networks (GNNs) to utilize graph structures in
brains, improving detection performances. Current methods use correlation
measures between ROI's blood-oxygen-level-dependent (BOLD) signals to generate
the graph structure. Other methods use the training samples to learn the
optimal graph structure through end-to-end learning. However, implementing
those methods independently leads to some issues with noisy data for the
correlation graphs and overfitting problems for the optimal graph. In this
work, we proposed Bargrain (balanced graph structure for brains), which models
two graph structures: filtered correlation matrix and optimal sample graph
using graph convolution networks (GCNs). This approach aims to get advantages
from both graphs and address the limitations of only relying on a single type
of structure. Based on our extensive experiment, Bargrain outperforms
state-of-the-art methods in classification tasks on brain disease datasets, as
measured by average F1 scores.

本研究提出了 Bargrain，它通过使用图卷积网络模拟了两种图结构：过滤后的相关矩阵和最优样本图，旨在既得益于这两种图结构，又解决了仅依赖单一结构的限制。大量实验证明，Bargrain 在脑部疾病数据集的分类任务中，以平均 F1 分数为衡量指标，胜过了现有方法。

平衡图结构信息用于脑疾病检测

Balanced Graph Structure Information for Brain Disease Detection

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of
real-world applications by relying on the fixed graph data as input. However,
the initial input graph might not be optimal in terms of specific downstream
tasks, because of information scarcity, noise, adversarial attacks, or
discrepancies between the distribution in graph topology, features, and
groundtruth labels. In this paper, we propose a bi-level optimization-based
approach for learning the optimal graph structure via directly learning the
Personalized PageRank propagation matrix as well as the downstream
semi-supervised node classification simultaneously. We also explore a low-rank
approximation model for further reducing the time complexity. Empirical
evaluations show the superior efficacy and robustness of the proposed model
over all baseline methods.

本文提出一种基于双层优化的方法，通过直接学习个性化 PageRank 传播矩阵以及下游半监督节点分类并同时进行学习，学习最佳的图结构，并探索低秩逼近模型以进一步降低时间复杂度。实证评估表明，所提出的模型在所有基线方法中具有更高的有效性和鲁棒性。