Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance

2025/12/18 by Shiwakoti, Shikshya, Goldsmith, Samuel, Pandit, Ujjwal
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Graph Theory and Algorithms #Information Retrieval (cs.IR) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2512.16661

openalex publication_date 2025/12/18 · openalex created_date 2025/12/21 · openalex updated_date 2026/07/29

Abstract

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.

Citations

• Retrieval-Augmented Generation with Graphs (GraphRAG)
• GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Self-Attention Graph Pooling
• Graph Attention Networks

Related

• A Bird's-Eye Tutorial of Graph Attention Architectures
• Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
• GRAG: Graph Retrieval-Augmented Generation
• GPR: Empowering Generation with Graph-Pretrained Retriever
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models