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arXiv · 2508.05786

Functional Connectivity Graph Neural Networks

Yang Li·Luopeiwen Yi·Tananun Songdechakraiwut
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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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Yang Li, Luopeiwen Yi, Tananun Songdechakraiwut. 2025-08-07. Functional Connectivity Graph Neural Networks. https://arxiv.org/abs/2508.05786

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