Title: Fine-Grained Graph Generation through Latent Mixture Scheduling
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Control Generation, Graph Generation

Abstract: Structure aware graph generation aims to generate graphs that satisfy given topological properties. It has applications in domains such as drug discovery, social network modeling, and knowledge graph construction. Unlike existing methods that only provide coarse control over graph properties, we introduce a novel conditional variational auto-encoder for fine-grained structural control in graph generation. The approach refines the decoder’s latent space by dynamically aligning graph- and property-driven representations to improve both graph fidelity and control satisfaction. Specifically, the approach implements a mixture scheduler that progressively integrates graph and control priors. Experiments on five real-world datasets show the efficacy of the proposed model compared to recent baselines, achieving high generation quality while maintaining high controllability.