Graffe: Graph Representation Learning via Diffusion Probabilistic Models

• URL: http://arxiv.org/abs/2505.04956v1
• Date: Thu, 08 May 2025 05:38:19 GMT
• Title: Graffe: Graph Representation Learning via Diffusion Probabilistic Models
• Authors: Dingshuo Chen, Shuchen Xue, Liuji Chen, Yingheng Wang, Qiang Liu, Shu Wu, Zhi-Ming Ma, Liang Wang,
• Abstract summary: We introduce Graffe, a self-supervised diffusion model proposed for graph representation learning.<n>It features a graph encoder that distills a source graph into a compact representation, which serves as the condition to guide the denoising process of the diffusion decoder.
• Score: 25.28957372847043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progress has highlighted their potential for capturing visual semantics, adapting DPMs to graph representation learning remains in its infancy. In this paper, we introduce Graffe, a self-supervised diffusion model proposed for graph representation learning. It features a graph encoder that distills a source graph into a compact representation, which, in turn, serves as the condition to guide the denoising process of the diffusion decoder. To evaluate the effectiveness of our model, we first explore the theoretical foundations of applying diffusion models to representation learning, proving that the denoising objective implicitly maximizes the conditional mutual information between data and its representation. Specifically, we prove that the negative logarithm of the denoising score matching loss is a tractable lower bound for the conditional mutual information. Empirically, we conduct a series of case studies to validate our theoretical insights. In addition, Graffe delivers competitive results under the linear probing setting on node and graph classification tasks, achieving state-of-the-art performance on 9 of the 11 real-world datasets. These findings indicate that powerful generative models, especially diffusion models, serve as an effective tool for graph representation learning.

Related papers

• Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs [52.50288418639075]
  We propose a novel framework called Iterative Denoising, which simplifies discrete diffusion and circumvents the issue by assuming conditional independence across time.<n>Our empirical evaluations demonstrate that the proposed method significantly outperforms existing discrete diffusion baselines in graph generation tasks.
  arXiv  Detail & Related papers  (2025-03-27T15:08:58Z)
• Graph Representation Learning with Diffusion Generative Models [0.0]
  We train a discrete diffusion model within an autoencoder framework to learn meaningful embeddings for graph data.<n>Our approach demonstrates the potential of discrete diffusion models to be used for graph representation learning.
  arXiv  Detail & Related papers  (2025-01-22T07:12:10Z)
• A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation [59.14165404728197]
  We provide an up-to-date and forward-looking review of deep graph learning under distribution shifts.<n>Specifically, we cover three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation.<n>To provide a better understanding of the literature, we introduce a systematic taxonomy that classifies existing methods into model-centric and data-centric approaches.
  arXiv  Detail & Related papers  (2024-10-25T02:39:56Z)
• PAC Learnability under Explanation-Preserving Graph Perturbations [15.83659369727204]
  Graph neural networks (GNNs) operate over graphs, enabling the model to leverage the complex relationships and dependencies in graph-structured data. A graph explanation is a subgraph which is an almost sufficient' statistic of the input graph with respect to its classification label. This work considers two methods for leveraging such perturbation invariances in the design and training of GNNs.
  arXiv  Detail & Related papers  (2024-02-07T17:23:15Z)
• Graph Out-of-Distribution Generalization with Controllable Data Augmentation [51.17476258673232]
  Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. Due to the selection bias of training and testing data, distribution deviation is widespread. We propose OOD calibration to measure the distribution deviation of virtual samples.
  arXiv  Detail & Related papers  (2023-08-16T13:10:27Z)
• Directional diffusion models for graph representation learning [9.457273750874357]
  We propose a new class of models called it directional diffusion models These models incorporate data-dependent, anisotropic, and directional noises in the forward diffusion process. We conduct extensive experiments on 12 publicly available datasets, focusing on two distinct graph representation learning tasks.
  arXiv  Detail & Related papers  (2023-06-22T21:27:48Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• 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)
• Robust Causal Graph Representation Learning against Confounding Effects [21.380907101361643]
  We propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders.
  arXiv  Detail & Related papers  (2022-08-18T01:31:25Z)
• Latent Augmentation For Better Graph Self-Supervised Learning [20.082614919182692]
  We argue that predictive models weaponed with latent augmentations and powerful decoder could achieve comparable or even better representation power than contrastive models. A novel graph decoder named Wiener Graph Deconvolutional Network is correspondingly designed to perform information reconstruction from augmented latent representations.
  arXiv  Detail & Related papers  (2022-06-26T17:41:59Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• OOD-GNN: Out-of-Distribution Generalized Graph Neural Network [73.67049248445277]
  Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution. Existing GNNs lack out-of-distribution generalization abilities so that their performance substantially degrades when there exist distribution shifts between testing and training graph data. We propose an out-of-distribution generalized graph neural network (OOD-GNN) for achieving satisfactory performance on unseen testing graphs that have different distributions with training graphs.
  arXiv  Detail & Related papers  (2021-12-07T16:29:10Z)

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.