Summary of Towards Understanding Link Predictor Generalizability Under Distribution Shifts, by Jay Revolinsky et al.
Towards Understanding Link Predictor Generalizability Under Distribution Shifts
by Jay Revolinsky, Harry Shomer, Jiliang Tang
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 State-of-the-art link prediction (LP) models have achieved impressive results on benchmark datasets. However, these datasets often assume a representative distribution of training, validation, and testing samples, which is typically not the case in real-world scenarios where uncontrolled factors can alter the dataset distribution. Recent work has primarily focused on node- and graph-level tasks, neglecting link-level tasks. To address this gap, we introduce LPShift, a novel splitting strategy that utilizes structural properties to induce controlled distribution shift. Our empirical evaluation of SOTA LP models on 16 LPShift variants demonstrates significant changes in model performance. Additionally, our experiments show the strong influence of graph structure on generalization methods’ success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Link prediction is an important task in machine learning that helps predict missing links between data points. Researchers have made great progress in this area, but most datasets assume certain conditions are met, which isn’t always true in real-life scenarios. This paper proposes a new way to split datasets, called LPShift, to make them more representative of real-world situations. They tested their approach on various link prediction models and found that it significantly improves performance. The study also highlights the importance of considering graph structure when trying to predict missing links. |
Keywords
» Artificial intelligence » Generalization » Machine learning
