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Flowette: Flow Matching with Graphette Priors for Graph Generation

arXiv:2602.23566v1 Announce Type: new Abstract: We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework, that employs a graph neural network based transformer to learn a velocity field defined over graph representations with node and edge attributes. Our model preserves topology through optimal transport based coupling, and long-range structural dependencies through regularisation. To incorporate domain driven structural priors, we introduce graphettes, a new probabilistic family of graph structure models that generalize graphons via controlled structural edits for motifs like rings, stars and trees. We theoretically analyze the coupling, invariance, and structural properties of the proposed framework, and empirically evaluate it on synthetic and small-molecule graph generation tasks. Flowette demonstrates consistent improvements, highlighting the effectiveness of combining structural priors with flow-based train

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Asiri Wijesinghe, Sevvandi Kandanaarachchi, Daniel M. Steinberg, Cheng Soon Ong · · 1 min read · 11 views
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arXiv:2602.23566v1 Announce Type: new Abstract: We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework, that employs a graph neural network based transformer to learn a velocity field defined over graph representations with node and edge attributes. Our model preserves topology through optimal transport based coupling, and long-range structural dependencies through regularisation. To incorporate domain driven structural priors, we introduce graphettes, a new probabilistic family of graph structure models that generalize graphons via controlled structural edits for motifs like rings, stars and trees. We theoretically analyze the coupling, invariance, and structural properties of the proposed framework, and empirically evaluate it on synthetic and small-molecule graph generation tasks. Flowette demonstrates consistent improvements, highlighting the effectiveness of combining structural priors with flow-based training for modeling complex graph distributions.

Executive Summary

The article presents Flowette, a novel framework for generative modeling of graphs with recurring subgraph motifs. Combining continuous flow matching with graph neural networks, Flowette preserves topology and captures long-range structural dependencies. The model incorporates domain-driven structural priors via graphettes, a probabilistic family of graph structure models. Theoretical analysis and empirical evaluation demonstrate Flowette's effectiveness in modeling complex graph distributions. The proposed framework exhibits consistent improvements over existing methods in synthetic and small-molecule graph generation tasks. The study contributes to the development of graph generative models and has significant implications for various applications, including chemistry and material science.

Key Points

  • ▸ Flowette integrates continuous flow matching with graph neural networks for graph generation
  • ▸ Graphettes, a probabilistic family of graph structure models, are used to incorporate domain-driven structural priors
  • ▸ Theoretical analysis and empirical evaluation demonstrate Flowette's effectiveness in modeling complex graph distributions
  • ▸ The proposed framework preserves topology and captures long-range structural dependencies

Merits

Strength in modeling complex graph distributions

Flowette demonstrates consistent improvements over existing methods in synthesizing and small-molecule graph generation tasks, highlighting its effectiveness in modeling complex graph distributions.

Preservation of topology and long-range structural dependencies

The proposed framework preserves topology through optimal transport-based coupling and captures long-range structural dependencies through regularization, enabling accurate modeling of real-world graphs.

Demerits

Limited scalability to large graph datasets

The empirical evaluation of Flowette is limited to small-molecule graph generation tasks and synthetic datasets, and its scalability to larger graph datasets remains an open question.

Difficulty in interpreting graphettes

The graphettes, a probabilistic family of graph structure models, can be challenging to interpret, especially for users without a background in graph theory and probability.

Expert Commentary

The article presents a well-motivated and well-executed study on the development of graph generative models. Flowette's ability to preserve topology and capture long-range structural dependencies is a significant contribution to the field of graph deep learning. However, the study's limitations, including its scalability to large graph datasets and the interpretability of graphettes, should be addressed in future work. Additionally, the empirical evaluation could be further strengthened by exploring more diverse graph datasets and model architectures.

Recommendations

  • ✓ Future studies should investigate the scalability of Flowette to larger graph datasets and explore more efficient optimization techniques for training the model.
  • ✓ Developing more interpretable representations of graphettes could facilitate the adoption of Flowette in applications where understanding graph structure is crucial.

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