6 papers
Measuring the Influence of Modality-Specific Node Representations on Graphs
Xu Wang, Xunkai Li, Yinlin Zhu +1
Multimodal attributed graphs combine graph structure with node text and image representations in recommendation and multimedia analysis. Graph predictors propagate these representa…
SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
Zhengyu Wu, Xu Wang, Hongchao Qin +4
Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities. Traditional graph learning contextualizes node semantics by c…
Multimodal Graph Negative Learning
Zhengyu Wu, Xu Wang, Hongchao Qin +4
Multimodal attributed graphs (MAGs) integrate graph topology with heterogeneous modality attributes, such as text and images, thereby enabling richer modeling of complex relational…
Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs
Sirui Zhang, Xu Wang, Zhengyu Wu +2
Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images. They support graph-centric tasks req…
GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
Xu Wang, Xunkai Li, Yinlin Zhu +2
Multimodal retrieval uses images, text, and object relationships to answer different questions about the same collection. A query may seek an object's paired description, another o…
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning
Naixing Xu, Qian Li, Xu Wang +2
Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With ri…