GraphRCG: Self-conditioned Graph Generation via Bootstrapped Representations

arXiv:2403.01071v1 Announce Type: new
Abstract: Graph generation generally aims to create new graphs that closely align with a specific graph distribution. Existing works often implicitly capture this distribution through the optimization of generators, potentially overlooking the intricacies of the distribution itself. Furthermore, these approaches generally neglect the insights offered by the learned distribution for graph generation. In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to …

abstract arxiv cs.ai cs.lg distribution generators graph graphs insights optimization through type via