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This research aims to improve node classification in graphs by integrating global structural context into a mixture of experts framework.

March 25, 2026EntropyOpen Access

SS-AdaMoE: Spatio-Spectral Adaptive Mixture of Experts with Global Structural Priors for Graph Node Classification

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Innovative framework enhances node classification in diverse graph types, indicating improved performance.

This research aims to improve node classification in graphs by integrating global structural context into a mixture of experts framework.
Proposed the SS-AdaMoE framework for node classification.
Utilized a Dual-Domain Expert System with spatial and spectral components.
Implemented a Hierarchical Global-Prior Gating Network to guide expert selection based on global graph structure.
Evaluated the model on five benchmark datasets, including both homophilic and heterophilic networks.
Achieved up to 2.65% accuracy improvement on the Chameleon dataset.
Outperformed the strongest MoE baseline by 1.41% on the Roman-empire dataset.
Surpassed traditional GCN architectures by over 28% on heterophilic datasets like Texas.

Kang et al. (2026) studied this question.

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