  Generative models for graph data are an important research topic in machine
learning. Graph data comprise two levels that are typically analyzed
separately: node-level properties such as the existence of a link between a
pair of nodes, and global aggregate graph-level statistics, such as motif
counts. This paper proposes a new multi-level framework that jointly models
node-level properties and graph-level statistics, as mutually reinforcing
sources of information. We introduce a new micro-macro training objective for
graph generation that combines node-level and graph-level losses. We utilize
the micro-macro objective to improve graph generation with a GraphVAE, a
well-established model based on graph-level latent variables, that provides
fast training and generation time for medium-sized graphs. Our experiments show
that adding micro-macro modeling to the GraphVAE model improves graph quality
scores up to 2 orders of magnitude on five benchmark datasets, while
maintaining the GraphVAE generation speed advantage.
