Permutation Invariant Graph Generation via Score-Based Generative Modeling

Understand

Learning generative models for graph-structured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of nodes.

• However, most of the existing generative models for graphs are not invariant to the chosen ordering, which might lead to an undesirable bias in the learned distribution.
• To address this difficulty, we propose a permutation invariant approach to modeling graphs, using the recent framework of score-based generative modeling.
• In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a.k.a., the score function).