Improving GNNs for Image Classification: Addressing Homophily Challenges
Graph Neural Networks (GNNs) are rapidly becoming essential tools in deep learning, but their effectiveness when applied to images is often limited by challenges in graph representation. Traditional image-to-graph conversions often result in structures with low homophily (dissimilar connected nodes)...
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| Auteurs principaux: | , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
IEEE
2025-01-01
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| Collection: | IEEE Open Journal of the Computer Society |
| Sujets: | |
| Accès en ligne: | https://ieeexplore.ieee.org/document/11195142/ |
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