Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
arXiv:2508.06588v3 Announce Type: replace Abstract: Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data. However, a fundamental challenge, i.e., codebook collapse, remains underexplored in the graph domain, significantly limiting the expressiveness and generalization of graph tokens.In this paper, we present an empirical study and observe that codebook collapse consistently occurs when training VQ jointly with Graph Neural Networks under graph reconstruction tasks, even with mitigation strategies p
The continuous evolution of AI research focuses on improving efficiency and robustness in representation learning, making advancements in vector quantization for graph data highly relevant.
Improving graph representation learning is crucial for advanced AI applications, as graphs are fundamental for modeling complex relationships in data, from social networks to molecular structures.
This research contributes to refining the methods for compressing and discretizing graph data representations, potentially leading to more efficient and generalizable graph neural networks.
- · AI researchers
- · Graph Neural Network developers
- · Data scientists
- · Inefficient graph representation methods
More robust and efficient Graph Neural Networks become viable for a wider range of applications.
Enhanced capabilities for analyzing complex, interconnected data structures across various industries, from drug discovery to cybersecurity.
Accelerated development of AI systems that can understand and reason over highly relational information, impacting fields like autonomous agents and scientific discovery.
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Read at arXiv cs.LG