mlsanityData to 7 Oct 2026

One Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction

arXiv 2604.18344 ↗First posted 20 Apr 2026cs.AIcited by 0

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Knowledge Graphs (KGs) are composed of triples, and the goal of Knowledge Graph Completion (KGC) is to infer the missing factual triples. Traditional KGC tasks predict missing elements in a triple given one or two of its elements. As a more realistic task, the Triple Set Prediction (TSP) task aims to infer the set of missing triples conditioned only on the observed knowledge graph, without assuming any partial information about the missing triples. Existing TSP methods predict the set of missing triples in a triple-by-triple manner, falling short in capturing the dependencies among the predicted triples to ensure consistency. To address this issue, we propose a novel discrete diffusion model termed DiffTSP that treats TSP as a generative task. DiffTSP progressively adds noise to the KG through a discrete diffusion process, achieved by masking relational edges. The reverse process then gradually recovers the complete KG conditioned on the incomplete graph. To this end, we design a structure-aware denoising network that integrates a relational context encoder with a relational graph diffusion transformer for knowledge graph generation. DiffTSP can generate the complete set of triples in a one-pass manner while ensuring the dependencies among the predicted triples. Our approach achieves state-of-the-art performance on three public datasets. Code: https://github.com/ADMIS-TONGJI/DiffTSP.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 32 of its 43 references that are in the map. First posted on arXiv 20 Apr 2026.

Builds on

32 of 43 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model13 Oct
  2. 2025Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models12 Mar
  3. 2025Large Language Diffusion Models14 Feb
  4. 2024DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning3 Nov

Other references · 28

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2022Scalable Diffusion Models with Transformerscited by 5,931
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  5. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  6. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  7. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  8. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  9. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  10. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  11. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
  12. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
16 more
  1. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  2. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
  3. 2019Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Predictioncited by 133
  4. 2023Towards Foundation Models for Knowledge Graph Reasoningcited by 83
  5. 2022Sequence-to-Sequence Knowledge Graph Completion and Question Answeringcited by 75
  6. 2020PairRE: Knowledge Graph Embeddings via Paired Relation Vectorscited by 67
  7. 2022A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphscited by 49
  8. 2024Graph Diffusion Transformers for Multi-Conditional Molecular Generationcited by 44
  9. 2021AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibabacited by 33
  10. 2021Meta-Knowledge Transfer for Inductive Knowledge Graph Embeddingcited by 31
  11. 2024Path-based Explanation for Knowledge Graph Completioncited by 11
  12. 2024Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesiscited by 10
  13. 2024Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completioncited by 7
  14. 2023Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphscited by 4
  15. 2025Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Modelcited by 3
  16. 2024Is Large Language Model Good at Triple Set Prediction? An Empirical Studycited by 1

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  3. 2026RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
  4. 2025TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion
  5. 2026Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion
  6. 2025DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
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