FedMPO recovers missing modalities via topology-aware generation, filters noisy recoveries with missing-aware routing, and uses reliability-aware aggregation to achieve up to 5.65% gains over baselines in high-missing and non-IID federated graph settings.
and Welling, Max , title =
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GraphInstruct introduces a six-level progressive benchmark with 800 instructions and 1,582 references to diagnose LLM graph generation gaps, plus a verification-guided iterative prompting framework that improves performance.
RAwR augments graphs with role-aware quotient graphs from approximate equitable partitions to accelerate long-range communication in GNNs, achieving SOTA results on homophilic, heterophilic, and long-range benchmarks while recovering master-node rewiring in the limit.
PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.
citing papers explorer
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Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
FedMPO recovers missing modalities via topology-aware generation, filters noisy recoveries with missing-aware routing, and uses reliability-aware aggregation to achieve up to 5.65% gains over baselines in high-missing and non-IID federated graph settings.
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GraphInstruct: A Progressive Benchmark for Diagnosing Capability Gaps in LLM Graph Generation
GraphInstruct introduces a six-level progressive benchmark with 800 instructions and 1,582 references to diagnose LLM graph generation gaps, plus a verification-guided iterative prompting framework that improves performance.
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RAwR: Role-Aware Rewiring via Approximate Equitable Partition
RAwR augments graphs with role-aware quotient graphs from approximate equitable partitions to accelerate long-range communication in GNNs, achieving SOTA results on homophilic, heterophilic, and long-range benchmarks while recovering master-node rewiring in the limit.
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PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.