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This is a Quantitative Study study.

October 16, 2025Open Access

Are GNNs doomed by the topology of their input graph?

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This work explores how topology affects GNNs, highlighting the interplay of local features and message-passing.

GNNs show effectiveness in learning but may be limited by the topology of their input graph, which can lead to oversmoothing.
Local topological features interact with the message-passing scheme, influencing the resulting node representations during learning.
Empirical experiments demonstrate that consistent node representations depend on the structure and similarity of local neighborhoods.
Understanding these interactions is significant for improving GNN performance in practical applications.

Aboussalah et al. (2025) studied this question.

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