Brain graphs, which model the structural and functional relationships between
brain regions, are crucial in neuroscientific and clinical applications
involving graph classification. However, dense brain graphs pose computational
challenges including high runtime and memory usage and limited
interpretability. In this paper, we investigate effective designs in Graph
Neural Networks (GNNs) to sparsify brain graphs by eliminating noisy edges.
While prior works remove noisy edges based on explainability or task-irrelevant
properties, their effectiveness in enhancing performance with sparsified graphs
is not guaranteed. Moreover, existing approaches often overlook collective edge
removal across multiple graphs.
To address these issues, we introduce an iterative framework to analyze
different sparsification models. Our findings are as follows: (i) methods
prioritizing interpretability may not be suitable for graph sparsification as
they can degrade GNNs' performance in graph classification tasks; (ii)
simultaneously learning edge selection with GNN training is more beneficial
than post-training; (iii) a shared edge selection across graphs outperforms
separate selection for each graph; and (iv) task-relevant gradient information
aids in edge selection. Based on these insights, we propose a new model,
Interpretable Graph Sparsification (IGS), which enhances graph classification
performance by up to 5.1% with 55.0% fewer edges. The retained edges identified
by IGS provide neuroscientific interpretations and are supported by
well-established literature.

本文介绍了一种新的框架，使用图神经网络 (GNNs) 中的有效设计来稀疏化脑图，以优化脑图分类任务。该新模型称作 “可解释的图稀疏化”（IGS），通过减少脑图的边数（最多 55.0%），提高了最多 5.1% 的分类性能，并提出了新的脑科学解释。

可解释性脑图稀疏化：图神经网络的更佳实践和有效设计

Interpretable Sparsification of Brain Graphs: Better Practices and  Effective Designs for Graph Neural Networks

Biological and social systems consist of myriad interacting units. The
interactions can be represented in the form of a graph or network. Measurements
of these graphs can reveal the underlying structure of these interactions,
which provides insight into the systems that generated the graphs. Moreover, in
applications such as connectomics, social networks, and genomics, graph data
are accompanied by contextualizing measures on each node. We utilize these node
covariates to help uncover latent communities in a graph, using a modification
of spectral clustering. Statistical guarantees are provided under a joint
mixture model that we call the node-contextualized stochastic blockmodel,
including a bound on the mis-clustering rate. The bound is used to derive
conditions for achieving perfect clustering. For most simulated cases,
covariate-assisted spectral clustering yields results superior to regularized
spectral clustering without node covariates and to an adaptation of canonical
correlation analysis. We apply our clustering method to large brain graphs
derived from diffusion MRI data, using the node locations or neurological
region membership as covariates. In both cases, covariate-assisted spectral
clustering yields clusters that are easier to interpret neurologically.

通过改进的谱聚类方法，利用节点协变量帮助揭示图中的潜在社区，该方法综合考虑了节点上的上下文信息，应用于在大脑扩散 MRI 数据中的大脑图研究中，得到了更易于在神经学上进行解释的社区聚类效果。