What Contributes More to the Robustness of Heterophilic Graph Neural Networks?
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DOI: 10.1109/tsmc.2025.3548860
Abstract: In recent years, graph neural networks (GNNs) have gained significant attention due to their outstanding performance on graph-related tasks by utilizing neighborhood aggregation. However, traditional GNNs are primarily designed based on the homophily assumption, which… read more here.
Keywords: contributes robustness; gnns; graph neural; heterophilic gnns ... See more keywords