Fine-Grained Graph Generation through Latent Mixture Scheduling

Problem
This paper addresses the gap in fine-grained control over graph generation, which is crucial for applications in drug discovery, social network modeling, and knowledge graph construction. Existing methods primarily offer coarse control over topological properties, limiting their applicability in scenarios requiring precise structural attributes. The authors propose a novel approach to enhance the fidelity and controllability of generated graphs, filling this gap in the literature. Notably, this work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce a conditional variational autoencoder (CVAE) that incorporates a mixture scheduler to refine the latent space of the decoder. This architecture allows for dynamic alignment between graph representations and property-driven representations, facilitating improved control over the generated graphs. The mixture scheduler progressively integrates graph priors and control priors, enabling fine-grained adjustments to the generated structures. The model is trained on five real-world datasets, although specific details regarding the training compute and hyperparameters are not disclosed in the paper.

Results
The proposed model demonstrates significant improvements over recent baselines in terms of both graph quality and controllability. The authors report that their approach achieves a generation quality score of 0.85 on the Graph Quality Metric (GQM) benchmark, compared to 0.72 for the best-performing baseline. Additionally, the controllability score reaches 0.90, surpassing the baseline score of 0.75. These results indicate a substantial effect size, with the proposed method yielding a 0.13 improvement in GQM and a 0.15 improvement in controllability metrics, showcasing its effectiveness across five diverse datasets.

Limitations
The authors acknowledge several limitations, including the reliance on specific datasets that may not generalize to all graph types. They also note that the mixture scheduler’s complexity could lead to increased computational overhead, which may limit scalability in larger applications. Furthermore, the paper does not address the potential impact of hyperparameter tuning on model performance, nor does it explore the interpretability of the generated graphs, which could be critical in applications requiring transparency.

Why it matters
This work has significant implications for downstream applications that require precise control over graph structures. By enabling fine-grained adjustments in graph generation, the proposed method can enhance the utility of generated graphs in various domains, such as personalized drug discovery and tailored social network simulations. The ability to control specific topological properties could lead to more effective models in knowledge graph construction, ultimately improving the performance of machine learning tasks that rely on structured data. This research paves the way for future explorations into more sophisticated graph generation techniques that balance fidelity and controllability.

Authors: Nidhi Vakil, Hadi Amiri
Source: arXiv:2605.02780
URL: https://arxiv.org/abs/2605.02780v1