PIMPC-GNN: Physics-Informed Multi-Phase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks
This addresses the issue of biased predictions in GNNs for imbalanced data, which is a domain-specific problem in graph learning, and while it introduces a novel method, it appears incremental as it combines existing physics-based concepts into a hybrid framework.
The paper tackles the problem of class-imbalanced node classification in graph neural networks, where minority classes are under-represented, by proposing PIMPC-GNN, a physics-informed multi-phase consensus framework that integrates thermodynamic diffusion, Kuramoto synchronisation, and spectral embedding, achieving gains of up to +12.7% in minority-class recall and +8.3% in balanced accuracy across five benchmark datasets.
Graph neural networks (GNNs) often struggle in class-imbalanced settings, where minority classes are under-represented and predictions are biased toward majorities. We propose \textbf{PIMPC-GNN}, a physics-informed multi-phase consensus framework for imbalanced node classification. Our method integrates three complementary dynamics: (i) thermodynamic diffusion, which spreads minority labels to capture long-range dependencies, (ii) Kuramoto synchronisation, which aligns minority nodes through oscillatory consensus, and (iii) spectral embedding, which separates classes via structural regularisation. These perspectives are combined through class-adaptive ensemble weighting and trained with an imbalance-aware loss that couples balanced cross-entropy with physics-based constraints. Across five benchmark datasets and imbalance ratios from 5-100, PIMPC-GNN outperforms 16 state-of-the-art baselines, achieving notable gains in minority-class recall (up to +12.7\%) and balanced accuracy (up to +8.3\%). Beyond empirical improvements, the framework also provides interpretable insights into consensus dynamics in graph learning. The code is available at \texttt{https://github.com/afofanah/PIMPC-GNN}.