NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification

Goal:
The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network.
Methods:
we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we use the graph attention layer to learn the node features and gets the node information of different nodes for ASD classification.
Results:
Compared with other models, the NF-GAT has significant advantages in terms of classification results.
Conclusions:
NF-GAT can be effectively used for ASD classification.