Vincent Jung, Alba Carballo-Castro, Yiming Qin, Lonneke van der Plas, Pascal Frossard · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28618 · open-access PDF
图形生成模型通常使用图形特定的转换器架构来共同更新节点和边缘功能,然而,这些架构并没有直接受益于最近的硬件意识注意的实现,限制了它们的可扩展性。 然而,适应性不是微妙的:边缘和图形水平的功能必须注入对面注意分数,图形面具必须得到尊重,动态边缘更新仍然是平方内存使用的主要来源。
Machine translation (Meta M2M100); the English original follows.
Graph generative models commonly use graph-specific Transformer 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, graph masks must be respected, and dynamic edge updates remain a major source of quadratic memory usage. We propose an edge-aware attention layer for graph generation that incorporates structural information through head-wise
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