Functional Connectivity Graph Neural Networks
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916886896181248 |
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| author | Li, Yang Yi, Luopeiwen Songdechakraiwut, Tananun |
| author_facet | Li, Yang Yi, Luopeiwen Songdechakraiwut, Tananun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Functional Connectivity Graph Neural Networks Li, Yang Yi, Luopeiwen Songdechakraiwut, Tananun Neural and Evolutionary Computing 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. |
| title | Functional Connectivity Graph Neural Networks |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2508.05786 |