<ul><li>This paper explores when and why graph attention mechanisms are effective in node classification tasks.</li><li>The theoretical analysis reveals that the effectiveness of graph attention mechanisms depends on the relative levels of structure noise and feature noise in graphs.</li><li>In situations where structure noise exceeds feature noise, graph attention mechanisms enhance classification performance.</li><li>A novel multi-layer Graph Attention Network (GAT) architecture is proposed, which outperforms single-layer GATs in achieving perfect node classification.</li></ul>

Understanding When and Why Graph Attention Mechanisms Work via Node Classification

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