Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models

• URL: http://arxiv.org/abs/2403.01535v3
• Date: Wed, 18 Sep 2024 14:20:32 GMT
• Title: Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
• Authors: Iakovos Evdaimon, Giannis Nikolentzos, Christos Xypolopoulos, Ahmed Kammoun, Michail Chatzianastasis, Hadi Abdine, Michalis Vazirgiannis,
• Abstract summary: 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.
• Score: 22.794561387716502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties. Existing methods often fall short in efficiently addressing this need as they struggle with the high-dimensional complexity and varied nature of graph properties. In this paper, 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. NGG employs a variational graph autoencoder for graph compression and a diffusion process in the latent vector space, guided by vectors summarizing graph statistics. We demonstrate NGG's versatility across various graph generation tasks, showing its capability to capture desired graph properties and generalize to unseen graphs. We also compare our generator to the graph generation capabilities of different LLMs. This work signifies a shift in graph generation methodologies, offering a more practical and efficient solution for generating diverse graphs with specific characteristics.

Related papers

• Dynamic and Textual Graph Generation Via Large-Scale LLM-based Agent Simulation [70.60461609393779]
  GraphAgent-Generator (GAG) is a novel simulation-based framework for dynamic graph generation. Our framework effectively replicates seven macro-level structural characteristics in established network science theories. It supports generating graphs with up to nearly 100,000 nodes or 10 million edges, with a minimum speed-up of 90.4%.
  arXiv  Detail & Related papers  (2024-10-13T12:57:08Z)
• Advancing Graph Generation through Beta Diffusion [49.49740940068255]
  Graph Beta Diffusion (GBD) is a generative model specifically designed to handle the diverse nature of graph data. We propose a modulation technique that enhances the realism of generated graphs by stabilizing critical graph topology.
  arXiv  Detail & Related papers  (2024-06-13T17:42:57Z)
• GraphRCG: Self-Conditioned Graph Generation [78.69810678803248]
  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.
  arXiv  Detail & Related papers  (2024-03-02T02:28:20Z)
• 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)
• An Accurate Graph Generative Model with Tunable Features [0.8192907805418583]
  We propose a method to improve the accuracy of GraphTune by adding a new mechanism to feed back errors of graph features. Experiments on a real-world graph dataset showed that the features in the generated graphs are accurately tuned compared with conventional models.
  arXiv  Detail & Related papers  (2023-09-03T12:34:15Z)
• Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling [20.618785908770356]
  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. We propose EDGE, a new diffusion-based generative graph model that addresses generative tasks with large graphs.
  arXiv  Detail & Related papers  (2023-05-06T18:32:27Z)
• Graph Generation with Diffusion Mixture [57.78958552860948]
  Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. We propose a generative framework that models the topology of graphs by explicitly learning the final graph structures of the diffusion process.
  arXiv  Detail & Related papers  (2023-02-07T17:07:46Z)
• GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation [43.196067037856515]
  Graph generative models have broad applications in biology, chemistry and social science. Current leading autoregressive models fail to capture the permutation invariance nature of graphs. We propose a continuous-time generative diffusion process for permutation invariant graph generation.
  arXiv  Detail & Related papers  (2022-12-04T15:12:44Z)
• Learning Graphon Autoencoders for Generative Graph Modeling [91.32624399902755]
  Graphon is a nonparametric model that generates graphs with arbitrary sizes and can be induced from graphs easily. We propose a novel framework called textitgraphon autoencoder to build an interpretable and scalable graph generative model. A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons.
  arXiv  Detail & Related papers  (2021-05-29T08:11:40Z)
• Dirichlet Graph Variational Autoencoder [65.94744123832338]
  We present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. Motivated by the low pass characteristics in balanced graph cut, we propose a new variant of GNN named Heatts to encode the input graph into cluster memberships.
  arXiv  Detail & Related papers  (2020-10-09T07:35:26Z)

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.