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

Shuaiqi Liu, Beibei Liang, Siqi Wang, Bing Li, Lidong Pan*, Shui Hua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)428-433
Number of pages6
JournalIEEE Open Journal of Engineering in Medicine and Biology
Publication statusPublished - 2024
Externally publishedYes


  • Autism spectrum disorder
  • classification
  • functional connectivity
  • graphical attention network


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