GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
📰 ArXiv cs.AI
Learn how GraphSculptor reduces computational costs in graph self-supervised learning by constructing a pre-training coreset, retaining 96% of downstream performance with 50% fewer graphs
Action Steps
- Apply GraphSculptor to your graph dataset to identify redundant graphs
- Subsample 50% of graphs using GraphSculptor to reduce computational costs
- Evaluate the downstream performance of your model using the subsampled dataset
- Compare the results with the original dataset to verify the retention of performance
- Use GraphSculptor to construct a pre-training coreset for your graph self-supervised learning model
Who Needs to Know This
Machine learning engineers and researchers working on graph self-supervised learning can benefit from GraphSculptor to optimize their models and reduce computational costs
Key Insight
💡 GraphSculptor can reduce computational costs in graph self-supervised learning by constructing a pre-training coreset that retains most of the downstream performance with fewer graphs
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🚀 Reduce computational costs in graph self-supervised learning with GraphSculptor! Retain 96% of downstream performance with 50% fewer graphs 📊
Key Takeaways
Learn how GraphSculptor reduces computational costs in graph self-supervised learning by constructing a pre-training coreset, retaining 96% of downstream performance with 50% fewer graphs
Full Article
Title: GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
Abstract:
arXiv:2605.01310v1 Announce Type: cross Abstract: Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets contain substantial redundancy-our analysis reveals that uniformly subsampling 50% of graphs retains over 96% of downstream performance. To exploit this redundancy, we introduce GraphSculptor for pre-training coreset construction. Unlike methods dependent on additional t
Abstract:
arXiv:2605.01310v1 Announce Type: cross Abstract: Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets contain substantial redundancy-our analysis reveals that uniformly subsampling 50% of graphs retains over 96% of downstream performance. To exploit this redundancy, we introduce GraphSculptor for pre-training coreset construction. Unlike methods dependent on additional t
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