GraphRCG: Self-Conditioned Graph Generation

• URL: http://arxiv.org/abs/2403.01071v2
• Date: Thu, 18 Jul 2024 06:05:58 GMT
• Title: GraphRCG: Self-Conditioned Graph Generation
• Authors: Song Wang, Zhen Tan, Xinyu Zhao, Tianlong Chen, Huan Liu, Jundong Li,
• Abstract summary: We propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions. Our framework demonstrates superior performance over existing state-of-the-art graph generation methods in terms of graph quality and fidelity to training data.
• Score: 78.69810678803248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph generation generally aims to create new graphs that closely align with a specific graph distribution. Existing works often implicitly capture this distribution through the optimization of generators, potentially overlooking the intricacies of the distribution itself. Furthermore, these approaches generally neglect the insights offered by the learned distribution for graph generation. In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to guide the generation process. We first perform self-conditioned modeling to capture the graph distributions by transforming each graph sample into a low-dimensional representation and optimizing a representation generator to create new representations reflective of the learned distribution. Subsequently, we leverage these bootstrapped representations as self-conditioned guidance for the generation process, thereby facilitating the generation of graphs that more accurately reflect the learned distributions. We conduct extensive experiments on generic and molecular graph datasets across various fields. Our framework demonstrates superior performance over existing state-of-the-art graph generation methods in terms of graph quality and fidelity to training data.

Related papers

• Random Walk Diffusion for Efficient Large-Scale Graph Generation [0.43108040967674194]
  We propose ARROW-Diff (AutoRegressive RandOm Walk Diffusion), a novel random walk-based diffusion approach for efficient large-scale graph generation. We demonstrate that ARROW-Diff can scale to large graphs efficiently, surpassing other baseline methods in terms of both generation time and multiple graph statistics.
  arXiv  Detail & Related papers  (2024-08-08T13:42:18Z)
• Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models [22.794561387716502]
  We introduce the Neural Graph Generator (NGG), a novel approach which utilizes conditioned latent diffusion models for graph generation. NGG demonstrates a remarkable capacity to model complex graph patterns, offering control over the graph generation process.
  arXiv  Detail & Related papers  (2024-03-03T15:28:47Z)
• Overcoming Order in Autoregressive Graph Generation [12.351817671944515]
  Graph generation is a fundamental problem in various domains, including chemistry and social networks. Recent work has shown that molecular graph generation using recurrent neural networks (RNNs) is advantageous compared to traditional generative approaches.
  arXiv  Detail & Related papers  (2024-02-04T09:58:22Z)
• GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? [7.330479039715941]
  Large-scale graphs with node attributes are increasingly common in various real-world applications. Traditional graph generation methods are limited in their capacity to handle these complex structures. This paper introduces a novel diffusion model, GraphMaker, specifically designed for generating large attributed graphs.
  arXiv  Detail & Related papers  (2023-10-20T22:12:46Z)
• HiGen: Hierarchical Graph Generative Networks [2.3931689873603603]
  Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. We propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion. This modular approach enables scalable graph generation for large and complex graphs.
  arXiv  Detail & Related papers  (2023-05-30T18:04:12Z)
• Spectral Augmentations for Graph Contrastive Learning [50.149996923976836]
  Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. We propose a set of well-motivated graph transformation operations to provide a bank of candidates when constructing augmentations for a graph contrastive objective.
  arXiv  Detail & Related papers  (2023-02-06T16:26:29Z)
• Generative Diffusion Models on Graphs: Methods and Applications [50.44334458963234]
  Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks. Graph generation is a crucial computational task on graphs with numerous real-world applications.
  arXiv  Detail & Related papers  (2023-02-06T06:58:17Z)
• Graph Condensation via Receptive Field Distribution Matching [61.71711656856704]
  This paper focuses on creating a small graph to represent the original graph, so that GNNs trained on the size-reduced graph can make accurate predictions. We view the original graph as a distribution of receptive fields and aim to synthesize a small graph whose receptive fields share a similar distribution.
  arXiv  Detail & Related papers  (2022-06-28T02:10:05Z)
• Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations [57.15855198512551]
  We propose a novel score-based generative model for graphs with a continuous-time framework. We show that our method is able to generate molecules that lie close to the training distribution yet do not violate the chemical valency rule.
  arXiv  Detail & Related papers  (2022-02-05T08:21:04Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.