AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation
This work addresses the misalignment between graph representations and the latent feature space of frozen large language models in graph retrieval-augmented generation. To bridge this gap, the authors propose AGE, a method built upon a masked self-supervised learning framework that encodes graphs using a Transformer architecture with text-like embeddings. A key innovation of AGE is its learnable node sampler, which adaptively identifies and bypasses hard-to-predict yet critical nodes to better align graph and textual embedding spaces. Evaluated on four heterogeneous GraphQA benchmark datasets, AGE substantially outperforms existing non-parametric retrieval approaches, achieving state-of-the-art accuracy.
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