Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
- URL: http://arxiv.org/abs/2403.01535v2
- Date: Tue, 23 Apr 2024 13:46:10 GMT
- Title: Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
- Authors: Iakovos Evdaimon, Giannis Nikolentzos, 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: 24.192931640371746
- 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. This work signifies a significant shift in graph generation methodologies, offering a more practical and efficient solution for generating diverse types of graphs with specific characteristics.
Related papers
- Advancing Graph Generation through Beta Diffusion [49.49740940068255]
Graph Beta Diffusion (GBD) is a diffusion-based generative model adept at capturing diverse graph structures.
We have developed a modulation technique that enhances the realism of the generated graphs by stabilizing the generation of critical graph structures.
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? [8.008021732866055]
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) - 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) - GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism.
The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain.
GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
arXiv Detail & Related papers (2020-07-07T16:51:39Z) - Block-Approximated Exponential Random Graphs [77.4792558024487]
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs.
We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions.
Our methods are scalable to sparse graphs consisting of millions of nodes.
arXiv Detail & Related papers (2020-02-14T11:42:16Z)
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.