FedMGS formalizes modality-imbalanced MM-FGL as latent semantic synthesis and uses availability-aware encoding, prototype-guided synthesis, and reliability-calibrated fusion to recover missing modalities, reporting up to 17.41% gains on four tasks.
Unigraph2: Learning a unified embedding space to bind multimodal graphs
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GraphMNL applies negative learning as cross-branch guidance in multimodal graphs to mitigate semantic imbalance without propagating bias from dominant branches.
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.
citing papers explorer
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Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
FedMGS formalizes modality-imbalanced MM-FGL as latent semantic synthesis and uses availability-aware encoding, prototype-guided synthesis, and reliability-calibrated fusion to recover missing modalities, reporting up to 17.41% gains on four 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.
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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.