HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering

TL;DR

HetGCoT is a novel framework that enhances large language models with heterogeneous graph information to improve interpretability and accuracy in academic question answering tasks.

Contribution

It introduces a method to convert graph structures into reasoning chains, an adaptive metapath selection mechanism, and a multi-step reasoning strategy for scholarly QA.

Findings

Outperforms state-of-the-art baselines on OpenAlex and DBLP datasets.

Demonstrates effective integration of graph data into LLM reasoning.

Shows adaptability across different LLM architectures.

Abstract

Academic question answering (QA) in heterogeneous scholarly networks presents unique challenges requiring both structural understanding and interpretable reasoning. While graph neural networks (GNNs) capture structured graph information and large language models (LLMs) demonstrate strong capabilities in semantic comprehension, current approaches lack integration at the reasoning level. We propose HetGCoT, a framework enabling LLMs to effectively leverage and learn information from graphs to reason interpretable academic QA results. Our framework introduces three technical contributions: (1) a framework that transforms heterogeneous graph structural information into LLM-processable reasoning chains, (2) an adaptive metapath selection mechanism identifying relevant subgraphs for specific queries, and (3) a multi-step reasoning strategy systematically incorporating graph contexts into the reasoning process. Experiments on OpenAlex and DBLP datasets show our approach outperforms all sota baselines. The framework demonstrates adaptability across different LLM architectures and applicability to various scholarly question answering tasks.