RARD: Rationale-First Blockwise Autoregressive Diffusion in Rationale?Dominated Graph Generation

Graph generation underlies many critical applications, from social network modeling to knowledge graph reasoning. Across these diverse domains, many graphs are rationale–dominated : a small, semantically meaningful subgraph determines the property of interest, while the remaining edges contribute largely noisy variation. Despite the significance of this inherent structure, existing generative methods often fail to preserve these task–critical substructures. We introduce RARD (Rationale-first blockwise AutoRegressive Diffusion), a topology-guided framework that learns to separate and prioritize the rationale. RARD employs a persistent-homology-based learning objective to discover an optimal graph filtration, an edge ordering that explicitly separates rationale from noise. Building upon this learned filtration, RARD generates graphs blockwise: it adds filtration-aligned blocks autoregressively and refines each new block with a shared local discrete diffusion module, ensuring the rationale appears early while peripheral structure is added later. We provide theoretical analysis showing that maximizing the topological gap yields rationale-first ordering and collapses to a two-level filtration. Comprehensive experiments across seven benchmarks demonstrate that RARD achieves state-of-the-art performance on widely used metrics.