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.
Openmag: A comprehensive benchmark for multimodal- attributed graph
3 Pith papers cite this work. Polarity classification is still indexing.
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RoleMAG learns neighbor roles in multimodal graphs to route shared, complementary, and heterophilous signals through separate channels, improving propagation without modality interference.
CAMPA resolves modal conflicts in decoupled multimodal GNNs via cross-modal aligned propagation and trajectory aligned aggregation, outperforming coupled and decoupled baselines on benchmarks while retaining efficiency.
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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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RoleMAG: Learning Neighbor Roles in Multimodal Graphs
RoleMAG learns neighbor roles in multimodal graphs to route shared, complementary, and heterophilous signals through separate channels, improving propagation without modality interference.
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CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
CAMPA resolves modal conflicts in decoupled multimodal GNNs via cross-modal aligned propagation and trajectory aligned aggregation, outperforming coupled and decoupled baselines on benchmarks while retaining efficiency.