Summary of Diffusion-based Negative Sampling on Graphs For Link Prediction, by Trung-kien Nguyen et al.
Diffusion-based Negative Sampling on Graphs for Link Prediction
by Trung-Kien Nguyen, Yuan Fang
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Link prediction is a crucial task in graph analysis, with significant applications in social network analysis and recommendation systems. Modern methods employ contrastive approaches to learn robust node representations, where negative sampling plays a pivotal role. Typical negative sampling strategies aim to retrieve hard examples using predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. To address these limitations, we propose a novel strategy called Conditional Diffusion-based Multi-level Negative Sampling (DMNS), which leverages the Markov chain property of diffusion models to generate negative nodes in multiple levels of variable hardness and reconcile them for effective graph link prediction. Our method follows the sub-linear positivity principle for robust negative sampling. DMNS is demonstrated to be effective on several benchmark datasets, including [insert dataset names]. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about predicting connections between things on a network. This is important because it helps us understand how social networks work and make better recommendations for what people might like. Current methods use a special way of learning about these networks called contrastive learning, but they often struggle to find good examples to compare with. To fix this, the researchers developed a new method that creates many different levels of “hard” examples from the network’s hidden structure. This allows their method, called DMNS, to be more accurate and effective at predicting connections. The results show that DMNS is better than previous methods on several big datasets. |
Keywords
* Artificial intelligence * Diffusion
