Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning

ArXi:2508.06588v3 Announce Type: replace-cross 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