Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models

Researchers propose a lightweight autoregressive framework for graph generation that achieves near log-linear complexity by using structure-guided topological ordering, addressing scalability limitations in current diffusion and autoregressive models. The two-phase training strategy reduces overfitting and promotes novel graph generation while maintaining validity, with applications spanning molecular discovery, circuit design, and cybersecurity.

Graph generation models face a fundamental challenge: scaling to real-world complexity without sacrificing novelty. Current approaches either rely on computationally expensive diffusion methods requiring full-adjacency operations, or autoregressive models with quadratic or higher complexity that tend to memorize training data rather than generalize. This research addresses both problems through an elegant architectural innovation—converting graphs into serialized edge sequences via structure-guided topological ordering, enabling efficient sequence modeling with near log-linear computational requirements.

The breakthrough lies not just in efficiency but in combating overfitting, a persistent issue in generative modeling. By combining exploration-oriented data augmentation with iterative refinement during training, the framework forces the model to learn generalizable patterns rather than reproducing training examples. This dual approach to novelty—architectural efficiency paired with training methodology—represents a meaningful advance over prior work that treats these as separate concerns.

The practical implications extend across multiple domains. In molecular discovery, faster and more novel graph generation accelerates drug candidate screening. For circuit design and cybersecurity applications, the ability to generate diverse, structurally sound graphs enables better exploration of design spaces and threat scenarios. The framework's flexibility—supporting both LSTM and Mamba-style sequence backbones—demonstrates architectural agnosticity that could enable future optimizations as foundational models evolve.

The integration with large-memory accelerators hints at the direction of the field: specialized hardware enabling longer sequence experiments previously impossible on standard GPUs. This trend suggests emerging opportunities for infrastructure providers supporting advanced ML workloads.

• →Proposed framework achieves near log-linear complexity through structure-guided serialization, dramatically improving scalability over existing quadratic-complexity models.
• →Two-phase training combining exploration-oriented augmentation and iterative refinement reduces overfitting and improves novelty in generated graphs.
• →Maintains high validity and uniqueness metrics while improving novelty, addressing the core trade-off in graph generation.
• →Architecture supports multiple sequence backbone types (LSTM, Mamba), enabling future optimization as models evolve.
• →Applications span molecular discovery, circuit design, and cybersecurity—domains where graph generation efficiency directly impacts research velocity.