<ul><li>Graph Neural Networks (GNNs) are powerful for processing data in graph structures and have been successful in various applications.</li><li>Structural representation of each node, capturing rich local topological information, is crucial for enhancing performance in graph classification benchmarks.</li><li>The research introduces Graph Structure Attention Network (GSAT) which leverages structural information modeled by anonymous random walks (ARWs) to integrate with graph attention networks (GAT) for improved graph representation.</li><li>Experiments demonstrate GSAT slightly enhances the State-of-the-Art (SOTA) on certain graph classification benchmarks.</li></ul>

GSAT: Graph Structure Attention Networks

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