AI RESEARCH
Layer Embedding Deep Fusion Graph Neural Network
arXiv CS.LG
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ArXi:2604.23324v1 Announce Type: new Graph Neural Networks (GNNs) have nstrated impressive performance in learning representations from graph-structured data. However, their message-passing mechanism inherently relies on the assumption of label consistency among connected nodes, limiting their applicability to low-homophily settings. Moreover, since message passing operates as a hierarchical diffusion process, GNNs face challenges in capturing long-range dependencies.