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This research examines how uncertainty in edge labels affects message passing in Graph Neural Networks, particularly in heterophilic graphs.

Uncertainty-Aware Adaptive Edge Blocking in Heterophilic Graph Neural Networks

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This research examines how uncertainty in edge labels affects message passing in Graph Neural Networks, particularly in heterophilic graphs.
Analyzed neighbor propagation without treating self-loops as edge-type observations.
Estimated local homophily and edge classification error rate during training.
Used a row-normalized operator for the neighbor process.
The adaptive blocking strategy improved performance in signed Graph Neural Network backbones.
Consistently performed competitively compared to strong heterophily-oriented baselines.
Provided theoretical guarantees for the selective blocking rule.

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This randomized trial demonstrates improved message passing strategies in heterophilic graphs, suggesting enhanced graph classification accuracy.

Choi et al. (2026) studied this question.

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This randomized trial demonstrates improved message passing strategies in heterophilic graphs, suggesting enhanced graph classification accuracy.