Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
Diffusion-based generative graph models have been proven effective in generating high-quality small graphs. However, they need to be more scalable for generating large graphs containing thousands of nodes desiring graph statistics. In this work, we propose EDGE, a new diffusion-based generative graph model that addresses generative tasks with large graphs. To improve computation efficiency, we encourage graph sparsity by using a discrete diffusion process that randomly removes edges at each time step and finally obtains an empty graph. EDGE only focuses on a portion of nodes in the graph at each denoising step. It makes much fewer edge predictions than previous diffusion-based models. Moreover, EDGE admits explicitly modeling the node degrees of the graphs, further improving the model performance. The empirical study shows that EDGE is much more efficient than competing methods and can generate large graphs with thousands of nodes. It also outperforms baseline models in generation quality: graphs generated by our approach have more similar graph statistics to those of the training graphs.
Code (1)
Tasks
DenoisingGraph GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models
Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely adopted due to the discrete nature of 2D m…
Graph GenerationDrug DiscoveryCoPHo: Classifier-guided Conditional Topology Generation with Persistent Homology
The structure of topology underpins much of the research on performance and robustness, yet available topology data are typically scarce, necessitating the generation of synthetic graphs with desired properties for testi…
BrepGiff: Lightweight Generation of Complex B-rep with 3D GAT Diffusion
Despite advancements in Computer-Aided-Design (CAD) generation, direct generation of complex Boundary Representation (B-rep) CAD models remains challenging. This difficulty arises from the parametric nature of B-rep …
Graph AttentionGraph Guided Diffusion: Unified Guidance for Conditional Graph Generation
Diffusion models have emerged as powerful generative models for graph generation, yet their use for conditional graph generation remains a fundamental challenge. In particular, guiding diffusion models on graphs under ar…
Computational EfficiencyFairnessGraph GenerationLink PredictionGraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories
Given a set of graphs from some unknown family, we want to generate new graphs from that family. Recent methods use diffusion on either graph embeddings or the discrete space of nodes and edges. However, simple changes t…
Graph Generation