mlsanityData to 7 Oct 2026

Sub-graph Based Diffusion Model for Link Prediction

arXiv 2409.08487 ↗First posted 13 Sep 2024cs.LG cs.AI stat.MLcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood. These models work by traversing a forward Markov Chain where data is perturbed, followed by a reverse process where a neural network learns to undo the perturbations and recover the original data. There have been increasing efforts exploring the applications of DDPMs in the graph domain. However, most of them have focused on the generative perspective. In this paper, we aim to build a novel generative model for link prediction. In particular, we treat link prediction between a pair of nodes as a conditional likelihood estimation of its enclosing sub-graph. With a dedicated design to decompose the likelihood estimation process via the Bayesian formula, we are able to separate the estimation of sub-graph structure and its node features. Such designs allow our model to simultaneously enjoy the advantages of inductive learning and the strong generalization capability. Remarkably, comprehensive experiments across various datasets validate that our proposed method presents numerous advantages: (1) transferability across datasets without retraining, (2) promising generalization on limited training data, and (3) robustness against graph adversarial attacks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 51 of its 63 references that are in the map. First posted on arXiv 13 Sep 2024.

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51 of 63 references.

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  1. 2023Generative Diffusion Models on Graphs: Methods and Applications6 Feb
  2. 2022DiGress: Discrete Denoising diffusion for graph generation29 Sep

Other references · 49

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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  10. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  11. 2017Inductive Representation Learning on Large Graphscited by 6,284
  12. 2022Generative Adversarial Networkscited by 5,603
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  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  3. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  4. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  5. 2016Variational Graph Auto-Encoderscited by 1,373
  6. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
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  8. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  9. 2018Link Prediction Based on Graph Neural Networkscited by 829
  10. 2023AudioLDM: Text-to-Audio Generation with Latent Diffusion Modelscited by 592
  11. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 402
  12. 2023Your Diffusion Model is Secretly a Zero-Shot Classifiercited by 301
  13. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
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  19. 2023Text-to-Image Diffusion Models are Zero-Shot Classifierscited by 140
  20. 2019Classification Accuracy Score for Conditional Generative Modelscited by 134
  21. 2018DeepInf: Social Influence Prediction with Deep Learningcited by 128
  22. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
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  24. 2022Graph Neural Networks for Link Prediction with Subgraph Sketchingcited by 81
  25. 2023Robust Classification via a Single Diffusion Modelcited by 79
  26. 2020DeepRobust: A PyTorch Library for Adversarial Attacks and Defensescited by 78
  27. 2022Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Predictioncited by 78
  28. 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarkingcited by 69
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  30. 2023Diffusion Models Beat GANs on Image Classificationcited by 57
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  4. 2024An overview of diffusion models for generative artificial intelligence
  5. 2024Denoising Diffusion Probabilistic Models in Six Simple Steps
  6. 2023Autoregressive Diffusion Model for Graph Generation
  7. 2023SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space
  8. 2026Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph