Fine-Grained Graph Generation with Latent Mixture Scheduling

Controlled graph generation requires producing graphs whose structures satisfy given topological attributes, instead of merely matching an unconditional graph distribution. Yet conditional latent-variable models can suffer from a mismatch between graph-conditioned latent representations used during training and attribute-conditioned representations used at inference. We introduce TOPOGEN, a conditional stochastic graph generator for fine-grained structural control. Its central component is a latent mixture scheduler that gradually transitions training from graph-conditioned training to attribute-only generation by interpolating latent distributions. This schedule reduces the mismatch between posterior-based reconstruction during training and prior-based conditional generation at inference time, enabling TOPOGEN to generate graphs from attributes alone. The approach allows improving joint constraint satisfaction without sacrificing diversity among valid outputs. Across five graph benchmark collections, TOPOGEN achieves the lowest average paired attribute error on all datasets and the best average structural fidelity across spectral and edit-distance metrics. Ablations show that latent scheduling and distribution alignment are important for controllable generation.