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Kernel entropy graph isomorphism network for graph classification
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DOI：10.1016/j.patcog.2026.113182.png)
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Abstract
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• We propose a fusion method for graph neural networks and graph kernels based on the attention mechanism. • We use a shortest path matching kernel to extract global structural features of graph. • We adopt an entropy-based graph isomorphism network to extract local structural features of graph. • We utilize the Nystom method kernel matrix for reduced-rank decomposition, which reduces the time consumed for model computation.
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