AI RESEARCH
Estimating Subgraph Importance with Structural Prior Domain Knowledge
arXiv CS.LG
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ArXi:2605.12009v1 Announce Type: new We propose a subgraph importance estimation method for pretrained Graph Neural Networks (GNNs) on graph-level tasks, formulated as a linear Group Lasso regression problem in the embedding space. Our method effectively leverages prior domain knowledge of graph substructures, while remaining independent of the specific form of the output layer or readout function used in the GNN architecture, and it does not require access to ground-truth target labels.