Using graphon limits for dense graphs, the authors decompose cross-domain output shifts for Lipschitz backbones into graph-specific finite-sample terms and a relabeling-invariant domain discrepancy, with stability results for spectral positional encodings.
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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.
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
Reevaluation of 9 GFMs shows only recent prior-data fitted network models outperform tuned GNNs on node property prediction, at higher cost.
ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
GNNs succeed in EDA when their propagation, aggregation, and supervision match the native algebra of each circuit task, such as max-plus recurrences for timing or hypergraph penalties for placement.
citing papers explorer
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When Do Graph Foundation Models Transfer? A Data-Centric Theory
Using graphon limits for dense graphs, the authors decompose cross-domain output shifts for Lipschitz backbones into graph-specific finite-sample terms and a relabeling-invariant domain discrepancy, with stability results for spectral positional encodings.
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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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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
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A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Reevaluation of 9 GFMs shows only recent prior-data fitted network models outperform tuned GNNs on node property prediction, at higher cost.
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Bridging Input Feature Spaces Towards Graph Foundation Models
ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
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Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
GNNs succeed in EDA when their propagation, aggregation, and supervision match the native algebra of each circuit task, such as max-plus recurrences for timing or hypergraph penalties for placement.