<ul><li>Graph neural networks (GNNs) have shown significant success in learning graph representations.</li><li>However, GNNs often fail to outperform simple MLPs on heterophilous graph tasks.</li><li>To overcome this, the Granular and Implicit Graph Network (GRAIN) is proposed, a novel GNN model specifically designed for heterophilous graphs.</li><li>GRAIN effectively integrates local and global information, resulting in improved node representations.</li></ul>

GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphs

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