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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
baseline 1polarities
baseline 1representative citing papers
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
-
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.
-
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.