Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance

• URL: http://arxiv.org/abs/2512.16661v2
• Date: Sun, 21 Dec 2025 15:17:01 GMT
• Title: Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance
• Authors: Shikshya Shiwakoti, Samuel Goldsmith, Ujjwal Pandit,
• Abstract summary: Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases.<n>We propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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