Summary of Node Duplication Improves Cold-start Link Prediction, by Zhichun Guo et al.
Node Duplication Improves Cold-start Link Prediction
by Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Neil Shah, Nitesh V. Chawla
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel augmentation technique called NodeDup to improve the performance of Graph Neural Networks (GNNs) in Link Prediction (LP) tasks, particularly on low-degree nodes. The authors show that GNNs struggle with LP on low-degree nodes despite their overall strong performance. NodeDup duplicates low-degree nodes and creates links between nodes and their own duplicates before following a standard supervised LP training scheme. This “multi-view” perspective for low-degree nodes leads to significant improvements in LP performance without compromising performance on high-degree nodes. The authors demonstrate the effectiveness of NodeDup by achieving 38.49%, 13.34%, and 6.76% improvements on isolated, low-degree, and warm nodes, respectively, across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NodeDup is a simple yet effective way to improve GNNs’ performance in Link Prediction tasks, especially for users with few observed interactions. This technique duplicates low-degree nodes and creates links between nodes and their own duplicates before training. The result is a significant improvement in LP performance without sacrificing performance on high-degree nodes. |
Keywords
* Artificial intelligence * Supervised
