Recently brain networks have been widely adopted to study brain dynamics,
brain development and brain diseases. Graph representation learning techniques
on brain functional networks can facilitate the discovery of novel biomarkers
for clinical phenotypes and neurodegenerative diseases. However, current graph
learning techniques have several issues on brain network mining. Firstly, most
current graph learning models are designed for unsigned graph, which hinders
the analysis of many signed network data (e.g., brain functional networks).
Meanwhile, the insufficiency of brain network data limits the model performance
on clinical phenotypes predictions. Moreover, few of current graph learning
model is interpretable, which may not be capable to provide biological insights
for model outcomes. Here, we propose an interpretable hierarchical signed graph
representation learning model to extract graph-level representations from brain
functional networks, which can be used for different prediction tasks. In order
to further improve the model performance, we also propose a new strategy to
augment functional brain network data for contrastive learning. We evaluate
this framework on different classification and regression tasks using the data
from HCP and OASIS. Our results from extensive experiments demonstrate the
superiority of the proposed model compared to several state-of-the-art
techniques. Additionally, we use graph saliency maps, derived from these
prediction tasks, to demonstrate detection and interpretation of phenotypic
biomarkers.

提出一种可解释性的分层符号图表示学习模型，用于从脑功能网络中提取全局表示以用于不同的预测任务，并且通过数据扩充进一步提高其性能。通过在 HCP 和 OASIS 数据集上进行广泛实验的结果表明，该模型在预测任务中表现出优越性，并使用图形显着性图来检测和解释表型生物标志物。