Summary of Accurate Link Prediction For Edge-incomplete Graphs Via Pu Learning, by Junghun Kim et al.
Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning
by Junghun Kim, Ka Hyun Park, Hoyoung Yoon, U Kang
First submitted to arxiv on: 20 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); 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 In this paper, researchers tackle the problem of link prediction in edge-incomplete graphs, a common scenario where not all relationships are observed. They propose a novel method called PULL (Positive-Unlabeled Learning-based Link predictor) that addresses this challenge by treating observed edges as positive examples and unconnected node pairs as unlabeled ones. This approach prevents overfitting to the observed graph by introducing latent variables for every edge and leveraging expected graph structure. The authors demonstrate the effectiveness of PULL on five real-world datasets, outperforming baselines in predicting links. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Link prediction is important in social networks and citation networks. When adding friends to a social network, not all users are checked. This paper proposes a method called PULL that predicts missing relationships between entities in edge-incomplete graphs. It’s like trying to find missing connections in a puzzle. The method prevents overfitting by using special variables and expected graph structure. The results show that it works better than other methods on real-world data. |
Keywords
» Artificial intelligence » Overfitting
