Concetti Chiave
Multi-level negative sampling using diffusion models improves graph link prediction.
Sintesi
The content discusses the importance of link prediction in graph analysis, introduces the DMNS method for multi-level negative sampling, and provides theoretical analysis supporting the effectiveness of the approach. It outlines the training algorithm, complexity analysis, and experimental results on benchmark datasets.
Introduction
- Link prediction is crucial for graph analysis.
- Modern methods use contrastive learning with negative sampling.
- DMNS proposes multi-level negative sampling using diffusion models.
Negative Sampling Strategies
- Uniform sampling ignores quality.
- Heuristic methods select hard negatives.
- Automatic methods like GANs aim for harder examples.
- DMNS introduces multi-level negative sampling for flexibility.
Diffusion-based Sampling
- Diffusion models generate negative nodes at different hardness levels.
- Conditional diffusion model conditions on query node for sampling.
- Theoretical analysis shows sub-linear positivity principle adherence.
Training Algorithm
- Alternating training of GNN and diffusion model.
- Diffusion loss minimization for noise prediction.
- Multi-level negative node sampling for link prediction.
Experimental Results
- Evaluation on benchmark datasets.
- Comparison with various baselines.
- DMNS outperforms other methods in most cases.
Statistiche
DMNS follows the sub-linear positivity principle for robust negative sampling.
Citazioni
"Our method, called Conditional Diffusion-based Multi-level Negative Sampling (DMNS), leverages the Markov chain property of diffusion models to generate negative nodes in multiple levels of variable hardness."
"We further demonstrate that DMNS follows the sub-linear positivity principle for robust negative sampling."