GRL-Safety benchmark shows that safety in graph representation learning depends on interactions between method design and specific graph stresses rather than broad method families.
Graph foundation models: A comprehensive survey
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Graphlets mined as structural tokens improve zero-shot inductive and transductive link prediction in knowledge graph foundation models across 51 diverse graphs.
DNSD replaces the sheaf Laplacian with a sheaf adjacency operator to maintain informative signals in deep layers, outperforming GNN and NSD baselines on long-range synthetic and real graph tasks.
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
SCGFM creates transferable graph representations by aligning heterogeneous topologies to shared learnable geometric bases via Gromov-Wasserstein distances and re-encoding features accordingly.
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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On the Safety of Graph Representation Learning
GRL-Safety benchmark shows that safety in graph representation learning depends on interactions between method design and specific graph stresses rather than broad method families.
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Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models
Graphlets mined as structural tokens improve zero-shot inductive and transductive link prediction in knowledge graph foundation models across 51 diverse graphs.
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Deep Neural Sheaf Diffusion
DNSD replaces the sheaf Laplacian with a sheaf adjacency operator to maintain informative signals in deep layers, outperforming GNN and NSD baselines on long-range synthetic and real graph tasks.
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A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
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Structure-Centric Graph Foundation Model via Geometric Bases
SCGFM creates transferable graph representations by aligning heterogeneous topologies to shared learnable geometric bases via Gromov-Wasserstein distances and re-encoding features accordingly.
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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.