Hourglass Persistence interleaves sequences of graph inclusions and contractions to produce more expressive topological features than standard persistent homology for learning on graphs and higher-order complexes.
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TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.
G-Defense builds claim-centered graphs from sub-claims, applies RAG for evidence and competing explanations, then uses graph inference to detect fake news veracity and generate intuitive explanation graphs, claiming SOTA results.
citing papers explorer
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Contraction and Hourglass Persistence for Learning on Graphs, Simplices, and Cells
Hourglass Persistence interleaves sequences of graph inclusions and contractions to produce more expressive topological features than standard persistent homology for learning on graphs and higher-order complexes.
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TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.
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A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
G-Defense builds claim-centered graphs from sub-claims, applies RAG for evidence and competing explanations, then uses graph inference to detect fake news veracity and generate intuitive explanation graphs, claiming SOTA results.