IFH: a Diffusion Framework for Flexible Design of Graph Generative Models

• URL: http://arxiv.org/abs/2408.13194v1
• Date: Fri, 23 Aug 2024 16:24:40 GMT
• Title: IFH: a Diffusion Framework for Flexible Design of Graph Generative Models
• Authors: Samuel Cognolato, Alessandro Sperduti, Luciano Serafini,
• Abstract summary: Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree.
• Score: 53.219279193440734
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. Ideally, between these two extreme models lies a continuous range of models that adopt different levels of sequentiality. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree. IFH is based upon the theory of Denoising Diffusion Probabilistic Models (DDPM), designing a node removal process that gradually destroys a graph. An insertion process learns to reverse this removal process by inserting arcs and nodes according to the specified sequentiality degree. We evaluate the performance of IFH in terms of quality, run time, and memory, depending on different sequentiality degrees. We also show that using DiGress, a diffusion-based one-shot model, as a generative step in IFH leads to improvement to the model itself, and is competitive with the current state-of-the-art.

Related papers

• 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)
• Autoregressive Diffusion Model for Graph Generation [12.390149720274904]
  We propose an emphautoregressive diffusion model for graph generation. Unlike existing methods, we define a node-absorbing diffusion process that operates directly in the discrete graph space. Our experiments on six diverse generic graph datasets and two molecule datasets show that our model achieves better or comparable generation performance with previous state-of-the-art.
  arXiv  Detail & Related papers  (2023-07-17T21:21:18Z)
• 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)
• 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)
• NVDiff: Graph Generation through the Diffusion of Node Vectors [20.424372965054832]
  We propose NVDiff, which takes the VGAE structure and uses a score-based generative model (SGM) as a flexible prior to sample node vectors. Built on the NVDiff framework, we introduce an attention-based score network capable of capturing both local and global contexts of graphs.
  arXiv  Detail & Related papers  (2022-11-19T20:43:39Z)
• DiGress: Discrete Denoising diffusion for graph generation [79.13904438217592]
  DiGress is a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. It achieves state-of-the-art performance on molecular and non-molecular datasets, with up to 3x validity improvement. It is also the first model to scale to the large GuacaMol dataset containing 1.3M drug-like molecules.
  arXiv  Detail & Related papers  (2022-09-29T12:55:03Z)
• 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)
• Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation [18.03898476141173]
  A graph generative model defines a distribution over graphs. We derive the exact joint probability over the graph and the node ordering of the sequential process. We train graph generative models by maximizing this bound, without using the ad-hoc node orderings of previous methods.
  arXiv  Detail & Related papers  (2021-06-11T06:37:52Z)

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.