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Conference Jul 2026

Quality-Aware Multimodal Graph Learning Via Attention-Guided Fusion and Reliability-Aware Optimization

Multimodal graph learning has become an important direction for modeling graph-structured data with heterogeneous node content, such as text and images. However, existing methods often rely on globally shared or weakly adaptive fusion strategies, which makes it difficult to model node-wise differences in modality usefu...

Shaohua Dong, Jia-Chun Chen, Wen-Jie Feng et al. · 0 citations
Conference Jul 2026

Relation-Aware Multi-View Representation Learning for Cross-KG Relation Prediction

Cross-KG relation prediction aims to predict possible relations between entities from different knowledge graphs. This task is difficult because different graphs often have different structures, use different relation types, and share only a small number of entities. Under this setting, it is not easy to pass useful in...

Jia-Rui Hu, Hao Ou, Bing Wei et al. · 0 citations

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