SP-ESGC decouples graph condensation into heat-kernel node condensation and pre-trained edge prediction for structure, claiming high efficiency and cross-GNN generalization on real-world datasets.
Graph neural networks: Methods, applications, and opportunities
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Logic-GNN induces symbolic grammars from clinical graphs via TGNN and Graph Kolmogorov Complexity, defining anomalies as MDL-expanding grammatical violations and reporting 0.94 F1 on the Sina dataset.
xAI-Drop introduces an explainability-based topological dropping regularizer for GNNs that outperforms state-of-the-art dropping methods in accuracy and explanation quality on real-world datasets.
citing papers explorer
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An Efficient and Scalable Graph Condensation with Structure-Preserving
SP-ESGC decouples graph condensation into heat-kernel node condensation and pre-trained edge prediction for structure, claiming high efficiency and cross-GNN generalization on real-world datasets.
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Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity
Logic-GNN induces symbolic grammars from clinical graphs via TGNN and Graph Kolmogorov Complexity, defining anomalies as MDL-expanding grammatical violations and reporting 0.94 F1 on the Sina dataset.
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xAI-Drop: Don't Use What You Cannot Explain
xAI-Drop introduces an explainability-based topological dropping regularizer for GNNs that outperforms state-of-the-art dropping methods in accuracy and explanation quality on real-world datasets.