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Multimodal graph predictors combine text, images, and relations to classify connected entities. How much of this input is needed to preserve their predictions? We study budgeted representation selection, which chooses a subset of candidate text and image vectors under a separate capacity for each modality. Predictions...
Graph-Optimized Multimodal Alignment (GOMA), which lets a jointly trained model support single-modality and dual-attribute retrieval through a task-specific readout, achieves state-of-the-art performance on all 14 primary measures against 14 external methods.
Multimodal attributed graphs connect entities, visual content, language, and observed relations. Learning one foundation across such graphs requires more than compressing each node into a fused Euclidean vector. The representation must preserve entity semantics, construct interaction state from graph neighborhoods, and...
Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clients must learn emerging classes from private multimodal graph streams, retain historical categories, and reject samples outside the known class space. In this setting, c...
Federated multimodal graph learning with Topology-aware Cross-modal Routing (FedTCR) is proposed, the first systematic algorithm designed for FMGL and outperforms state-of-the-art baselines on both graph-centric and modality-centric tasks.
A multimodal federated graph unlearning framework built around target-specific representation decoupling that effectively removes requested information, preserves retained graph utility, and achieves a speedup over full retraining.
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