Enhancing Graph Neural Networks via Memorized Global Information
Ruihong Zeng,
Jinyuan Fang,
Siwei Liu
et al.
Abstract:Graph neural networks (GNNs) have gained significant attention for their impressive results on different graph-based tasks. The essential mechanism of GNNs is the message-passing framework, whereby node representations are aggregated from local neighbourhoods. Recently, Transformer-based GNNs have been introduced to learn the long-range dependencies, enhancing performance. However, their quadratic computational complexity, due to the attention computation, has constrained their applicability on large-scale gra… Show more
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