GraphScale: Scaling Controlled Graph Generation through Hierarchical Constraint Composition

Controlled graph generation aims to generate graphs that satisfy specified structural constraints, yet existing methods largely trade off controllability and scalability: models that support fine-grained structural conditioning typically operate on small graphs, while scalable generators provide limited control over topology. We study whether global structural attributes can be factorized into local constraints and recovered through hierarchical composition. We introduce GraphScale, a model-agnostic framework that recursively decomposes target graph-level attributes into a hierarchy of subgraph constraints, generates leaf subgraphs independently, and composes them bottom-up using cross-subgraph connectivity conditioned on the remaining global structural attributes. This hierarchical factorization allows the underlying generator to operate only on bounded-size subgraphs while enabling the composed graph to scale to orders of magnitude larger sizes. Across real and synthetic datasets, GraphScale extends existing conditional graph generators to graphs with thousands to tens of thousands of nodes while retaining control over multiple structural properties and preserving global topology. These results demonstrate that compositional structural control offers a promising approach to scalable controlled graph generation.