The content discusses the importance of modeling and understanding how a trained GNN responds to graph evolution. It introduces a smooth parameterization approach using axiomatic attribution and differential geometric viewpoint. The proposed method aims to provide better sparsity, faithfulness, and intuitiveness in explaining GNN responses to evolving graphs through extensive experiments. The study focuses on node classification, link prediction, and graph classification tasks with evolving graphs. It compares various methods such as DeepLIFT, Grad, GNN-LRP, and AxiomPath-Convex for explaining the evolution of GNN predictions over evolving graphs.
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arxiv.org
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by Yazheng Liu,... klokken arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.06425.pdfDypere Spørsmål