Graph neural networks (GNNs) are widely used across domains but remain sensitive to class imbalance, class overlap, and complex data distributions, limiting reliability in real-world settings. Existing imbalance-mitigation strategies provide only partial robustness, are often computationally expensive, and leave post-h...
Olumayowa Onabanjo, Gemma Martinez Huerta, Carlos Francisco Moreno-García et al.· Figshare· 0 citations
Graph neural networks (GNNs) are widely used across domains but remain sensitive to class imbalance, class overlap, and complex data distributions, limiting reliability in real-world settings. Existing imbalance-mitigation strategies provide only partial robustness, are often computationally expensive, and leave post-h...
Olumayowa Onabanjo, Gemma Martinez Huerta, Carlos Francisco Moreno-García et al.· Figshare· 0 citations
Graph neural networks (GNNs) are widely used across domains but remain sensitive to class imbalance, class overlap, and complex data distributions, limiting reliability in real-world settings. Existing imbalance-mitigation strategies provide only partial robustness, are often computationally expensive, and leave post-h...
Olumayowa Onabanjo, Gemma Martínez Huerta, Carlos Francisco Moreno‐García et al.· Applied Artificial Intellige...· 0 citations
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