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Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance

arXiv 2512.16661 ↗First posted 18 Dec 2025cs.IR cs.AIcited by 0

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Abstract (arXiv)

In today's information-driven world, access to scientific publications has become increasingly easy. At the same time, filtering through the massive volume of available research has become more challenging than ever. Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases, particularly when combined with modern large language models. In this paper, we propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph, which is then passed to a large language model for advanced knowledge reasoning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 18 Dec 2025.

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References · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2017Graph Attention Networkscited by 8,923
  3. 2019Self-Attention Graph Poolingcited by 421
  4. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  5. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128

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  1. 2025Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
  2. 2026GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation
  3. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
  4. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
  5. 2024RAGraph: A General Retrieval-Augmented Graph Learning Framework
  6. 2025GPR: Empowering Generation with Graph-Pretrained Retriever
  7. 2026SeedER: Seed-Expand-Retrieve for Efficient Knowledge Graph Retrieval
  8. 2025Efficient Document Retrieval with G-Retriever