Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously explored alongside principal neural information retrieval approaches, such as two-phased retrieval, also known as re-ranking. While Graph Neural Networks (GNNs) have been proposed to demonstrate proficiency in graph learning for re-ranking, there are ongoing limitations in modeling and evaluating input graph structures for training and evaluation for passage and document ranking tasks. In this survey, we review emerging GNN-based ranking model architectures along with their corresponding graph representation construction methodologies. We conclude by providing recommendations on future research based on community-wide challenges and opportunities.
@article{arxiv.2503.14802,
title = {Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities},
author = {Md Shahir Zaoad and Niamat Zawad and Priyanka Ranade and Richard Krogman and Latifur Khan and James Holt},
journal= {arXiv preprint arXiv:2503.14802},
year = {2025}
}