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Balancing Symmetry and Efficiency in Graph Flow Matching
Benjamin Honoré, Alba Carballo-Castro, Yiming Qin +1
Equivariance is central to graph generative models, as it ensures the model respects the permutation symmetry of graphs. However, strict equivariance can increase computational cos…
Generating Directed Graphs with Dual Attention and Asymmetric Encoding
Alba Carballo-Castro, Manuel Madeira, Yiming Qin +2
Directed graphs naturally model systems with asymmetric, ordered relationships, essential to applications in biology, transportation, social networks, and visual understanding. Gen…
DeFoG: Discrete Flow Matching for Graph Generation
Yiming Qin, Manuel Madeira, Dorina Thanou +1
Graph generative models are essential across diverse scientific domains by capturing complex distributions over relational data. Among them, graph diffusion models achieve superior…
Sparse Training of Discrete Diffusion Models for Graph Generation
Yiming Qin, Clement Vignac, Pascal Frossard
Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs. To address the resulting quadratic complexity, existing…