Efficient Edge-aware Attention Network for Graph Generation
Graph generative models commonly use graph-specific AI architecture architectures to jointly update node and edge features. However, these architectures do not directly benefit from recent hardware-aware attention implementations, limiting their scalability. The adaptation is, however, non-trivial: edge and graph-level features must be injected into pairwise attention scores, g
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Graph generative models commonly use graph-specific AI architecture architectures to jointly update node and edge features. However, these architectures do not directly benefit from recent hardware-aware attention implementations, limiting their scalability. The adaptation is, however, non-trivial: edge and graph-level features must be injected into pairwise attention scores, g
This paper addresses a critical scalability bottleneck in graph generative models by proposing an efficient edge-aware attention layer. For ML engineers, understanding such optimizations is key to developing and deploying large-scale graph-based AI systems, especially in domains like drug discovery or material science where graph structures are prevalent.