Discrete Diffusion for Large Graph Generation via Structural Candidate Restricti
arXiv:2607.06646v1 Announce Type: cross Abstract: This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies
arXiv:2606.28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to
arXiv:2603.20470v1 Announce Type: new Abstract: The rapid growth of the text-to-image (T2I) community has fostered a thriving online ecosystem of expert models, which are variants
arXiv:2605.00354v1 Announce Type: cross Abstract: Many diffusion based molecule generation methods ignore the symbolic information of molecules and represent the atom and bond
arXiv:2607.20545v1 Announce Type: new Abstract: Diffusion models have become competitive generators for time series, but their practical use is limited by the large number of
arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic
arXiv:2605.06671v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph
arXiv:2603.22294v1 Announce Type: cross Abstract: Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an
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