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Related Experiment Video

Updated: Jan 10, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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DiffMLP: A diffusion-based multi-hop link prediction framework in knowledge graphs.

Hao Liu1, Dong Li1, Bing Zeng1

  • 1School of Software Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces DiffMLP, a new framework for multi-hop link prediction in knowledge graphs. DiffMLP enhances reasoning by modeling actions via a diffusion process, achieving state-of-the-art results.

Keywords:
Conditional denoiserDiffusion processKnowledge graph completionMulti-hop link prediction

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Graph Databases

Background:

  • Multi-hop link prediction in knowledge graphs is complex due to intricate reasoning paths and uncertainty.
  • Current methods struggle to model dependencies between reasoning context and neighborhood information.

Purpose of the Study:

  • To introduce DiffMLP, a novel framework for multi-hop link prediction.
  • To address limitations in modeling interactive dependencies in knowledge graph reasoning.

Main Methods:

  • DiffMLP models each hop's action space as a conditional distribution using a reverse diffusion process.
  • A graph attention-based denoiser, guided by reasoning context, refines action space embeddings.
  • Normalized noise injection and prior constraints stabilize and regulate the diffusion process.

Main Results:

  • DiffMLP achieved state-of-the-art performance on four benchmark datasets.
  • On FB15K-237, DiffMLP improved Mean Reciprocal Rank (MRR) by 7.0% and Hits@3 by 12.7% over prior best models.

Conclusions:

  • DiffMLP offers a significant advancement in multi-hop link prediction for knowledge graphs.
  • The diffusion-based approach effectively captures complex reasoning dependencies and uncertainty.