mlsanityData to 7 Oct 2026

AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation

arXiv 2607.00052 ↗First posted 30 Jun 2026cs.IR cs.AIcited by 0

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge. While this technique ideally captures intricate relationships, it often struggles with graph representations for LLMs, particularly for frozen LLMs, due to the misalignment between graph-based and text-based latent features. We tackle this issue by introducing the it Adaptive-masking for Graph Embedding (AGE). AGE employs a Transformer in a mask-based self-supervised learning (SSL) approach. We designed the architecture similar to text embedding encoders, addressing the latent feature misalignment. In contrast to natural language texts, graphs are concise representations, and there exist it key nodes that hold dominant contextual information, which are challenging to predict from their surroundings. Masking such key nodes leads to inefficiency in the SSL process. Therefore, AGE focuses on predicting nodes apart from key nodes, utilizing a learnable node sampler. Our experimental results indicate that AGE significantly improves approaches using non-parametric search component in GraphQA tasks, achieving superior accuracy across four benchmark datasets with distinct characteristics.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 67 of its 89 references that are in the map. First posted on arXiv 30 Jun 2026.

Builds on

67 of 89 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights17 Feb
  2. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)31 Dec
  3. 2024LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration6 Nov
  4. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning30 May
  5. 2024GRAG: Graph Retrieval-Augmented Generation26 May
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  7. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering12 Feb

Other references · 60

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  19. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 209
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  4. 2025Are Large Language Models In-Context Graph Learners?
  5. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
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