Summary of Label Informed Contrastive Pretraining For Node Importance Estimation on Knowledge Graphs, by Tianyu Zhang et al.
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
by Tianyu Zhang, Chengbin Hou, Rui Jiang, Xuegong Zhang, Chenghu Zhou, Ke Tang, Hairong Lv
First submitted to arxiv on: 26 Feb 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 Node Importance Estimation (NIE) in knowledge graphs aims to predict future or missing node importance scores. Traditional methods train models with available labels, treating all nodes equally. However, nodes with higher importance require more attention in real-world scenarios. To address this, we introduce Label Informed ContrAstive Pretraining (LICAP), a novel contrastive learning framework that utilizes continuous labels for pretraining embeddings. LICAP adopts top nodes preferred hierarchical sampling to generate contrastive samples and trains node embeddings using Predicate-aware Graph Attention Networks (PreGAT). This approach boosts the performance of existing NIE methods, achieving state-of-the-art results on regression and ranking metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Node Importance Estimation in knowledge graphs is like trying to figure out which movie or webpage is most important. Right now, machines learn by looking at what we already know, but that’s not always the best way. They should focus more on the really important things! That’s why we created a new method called LICAP, which helps machines learn more about the most important nodes in a graph. We use a special kind of machine learning called contrastive learning to help our approach work better. Our results show that this new method can make machines even better at predicting which nodes are most important. |
Keywords
* Artificial intelligence * Attention * Machine learning * Pretraining * Regression
