Abstract:The two-stage information retrieval (IR) framework, also known as the retrieve-then-re-rank pipeline, has empowered numerous AI paradigms, including retrieval-augmented generation (RAG) and question-answering (QA) systems that leverage ad hoc knowledge to address the rapidly expanding information landscape. In this setting, graph structures have emerged as a promising mechanism for context augmentation, further enhancing re-ranking frameworks through structured relational modeling, semantic dependency representation, and adaptive retrieval strategies. Consequently, graph representation learning techniques have been actively explored alongside leading IR paradigms. Despite increased research interest in graph-based re-ranking methods, a comprehensive study that connects existing approaches and provides a clear overview of this paradigm remains absent. In this survey, we provide an in-depth review of graph-based re-ranking models, tracing their history, evolution, and state-of-the-art development. To facilitate a clear understanding of this paradigm, we introduce an intuitive taxonomy that organizes graph-based re-ranking models by application domain and methodological characteristics. We also present a chronological timeline that illustrates the evolution of graph-based re-ranking methods. In addition, we analyze the experimental setups of representative studies and provide detailed insight into their findings. We conclude by providing recommendations on future research based on community-wide challenges and opportunities.
From: Md Shahir Zaoad [view email]
[v1]
Wed, 19 Mar 2025 00:28:54 UTC (273 KB)
[v2]
Fri, 24 Jul 2026 21:01:48 UTC (880 KB)
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