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.
Large language models meet text-attributed graphs: A survey of integration frameworks and applications.CoRR, abs/2510.21131
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N2NSC framework detects anomalies in text-attributed graphs by enforcing node-to-neighborhood semantic consistency via two complementary fusion paths that align textual semantics with topology.
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
citing papers explorer
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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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Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection
N2NSC framework detects anomalies in text-attributed graphs by enforcing node-to-neighborhood semantic consistency via two complementary fusion paths that align textual semantics with topology.
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Heterogeneous Scientific Foundation Model Collaboration
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.