arXiv:2604.08492v2 Announce Type: replace Abstract: Previous work has shown that node embedding methods can produce different representations and downstream predictions across repeated training runs, even when trained on the same data with identical hyperparameters. However, the role of embedding dimensionality in this instability remains poorly understood. In this work, we systematically analyze how embedding dimensionality affects the stability of embeddings from five widely used node embedding methods: ASNE, DGI, GraphSAGE, node2vec, and VERSE. We evaluate stability from both representation
This research is part of ongoing efforts to improve the reliability and robustness of machine learning models as AI systems become more complex and integrated into critical applications.
Understanding the stability of node embeddings is crucial for developing trustworthy and predictable graph-based AI models, impacting areas from drug discovery to social network analysis.
This research provides systematic insights into how embedding dimensionality affects the stability of node embeddings, offering guidance for researchers and practitioners in designing more stable AI systems.
- · AI researchers
- · Machine learning engineers
- · Graph AI developers
- · Developers of unstable AI systems
- · Applications reliant on highly variable graph embeddings
Improved reliability and reproducibility of node embedding models in various applications.
Faster development and deployment of robust AI systems built on graph data.
Increased trust in AI systems that use node embeddings, accelerating adoption in sensitive domains.
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Read at arXiv cs.LG