CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation

arXiv:2605.11468v1 Announce Type: new Abstract: Multimodal Graph Neural Networks (MGNNs) have shown strong potential for learning from multimodal attributed graphs, yet most existing approaches rely on tightly coupled architectures that suffer from prohibitive computational overhead. In this paper, we present a systematic empirical analysis showing that decoupled MGNNs are substantially more efficient and scalable for large-scale graph learning. However, we identify a critical bottleneck in exis