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.
arXiv preprint arXiv:2410.09132 , year =
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SMGFM decomposes multimodal node signals on graphs into frequency bands via Chebyshev filters, routes them by topology reliability, and aligns objectives to preserve modality-specific semantics while achieving SOTA on MAG tasks.
GraphMNL applies negative learning as cross-branch guidance in multimodal graphs to mitigate semantic imbalance without propagating bias from dominant branches.
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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SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
SMGFM decomposes multimodal node signals on graphs into frequency bands via Chebyshev filters, routes them by topology reliability, and aligns objectives to preserve modality-specific semantics while achieving SOTA on MAG tasks.
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Multimodal Graph Negative Learning
GraphMNL applies negative learning as cross-branch guidance in multimodal graphs to mitigate semantic imbalance without propagating bias from dominant branches.