Functional Connectivity Graph Neural Networks

• URL: http://arxiv.org/abs/2508.05786v1
• Date: Thu, 07 Aug 2025 18:59:02 GMT
• Title: Functional Connectivity Graph Neural Networks
• Authors: Yang Li, Luopeiwen Yi, Tananun Songdechakraiwut,
• Abstract summary: We propose a graph neural network framework that generalizes this approach to other domains.<n>Our method introduces a functional connectivity block based on persistent graph homology to capture global topological features.
• Score: 3.146587279468621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world networks often benefit from capturing both local and global interactions. Inspired by multi-modal analysis in brain imaging, where structural and functional connectivity offer complementary views of network organization, we propose a graph neural network framework that generalizes this approach to other domains. Our method introduces a functional connectivity block based on persistent graph homology to capture global topological features. Combined with structural information, this forms a multi-modal architecture called Functional Connectivity Graph Neural Networks. Experiments show consistent performance gains over existing methods, demonstrating the value of brain-inspired representations for graph-level classification across diverse networks.

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