arXiv Machine Learning
LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
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arXiv Machine Learning
Jun 17
SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
arXiv:2606. 12867v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities.
arXiv Machine Learning
Jun 15
Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs
arXiv:2606. 14172v1 Announce Type: new Abstract: Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images.
arXiv Machine Learning
Aug 10
TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
arXiv:2603. 27723v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks.
arXiv Machine Learning
Jul 20
Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
arXiv:2607. 15687v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs.
arXiv Machine Learning
Sep 25
GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
GOMA (Graph-Optimized Multimodal Alignment) introduces a dual-embedding approach for multimodal retrieval, separating content embeddings supervised for paired identity from semantic embeddings trained with cross-modal pairs and observed relationships. The method fuses these embeddings, applies semantic agreement to weight graph edges, and uses restart graph propagation to reinforce the initial signal, enabling both single-modality and dual-attribute retrieval. Across six datasets and four tasks, GOMA outperforms 14 external methods on 14 primary metrics, with controlled experiments highlighting the impact of separate supervision, graph regularization, and semantic-guided propagation.
Hugging Face Trending Papers
Jul 21
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.