  Despite the impressive advancements of Large Language Models (LLMs) in
generating text, they are often limited by the knowledge contained in the input
and prone to producing inaccurate or hallucinated content. To tackle these
issues, Retrieval-augmented Generation (RAG) is employed as an effective
strategy to enhance the available knowledge base and anchor the responses in
reality by pulling additional texts from external databases. In real-world
applications, texts are often linked through entities within a graph, such as
citations in academic papers or comments in social networks. This paper
exploits these topological relationships to guide the retrieval process in RAG.
Specifically, we explore two kinds of topological connections: proximity-based,
focusing on closely connected nodes, and role-based, which looks at nodes
sharing similar subgraph structures. Our empirical research confirms their
relevance to text relationships, leading us to develop a Topology-aware
Retrieval-augmented Generation framework. This framework includes a retrieval
module that selects texts based on their topological relationships and an
aggregation module that integrates these texts into prompts to stimulate LLMs
for text generation. We have curated established text-attributed networks and
conducted comprehensive experiments to validate the effectiveness of this
framework, demonstrating its potential to enhance RAG with topological
awareness.
