A contrastive, attention-based graph structure learning framework (DMGSL) that refines expert-built wireless data knowledge graphs and improves node classification accuracy on a real network dataset.
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Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
A contrastive, attention-based graph structure learning framework (DMGSL) that refines expert-built wireless data knowledge graphs and improves node classification accuracy on a real network dataset.