H et GC o T : Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025)

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.

## introduction academic question answering in heterogeneous scholarly networks presents essential challenges in integrating structural knowledge with semantic understanding. qa tasks regarding publishing venue selection, paper authorship, and scientific collaboration all require systems to reason over complex networks of papers, authors, venues, and organizations while providing interpretable explanations @xcite . the academic knowledge space is inherently heterogeneous, comprising diverse entities (e.g., papers, authors, venues and organizations) connected through various relationship types. effective academic question answering systems must address three fundamental challenges: (1) modeling heterogeneous structures to capture complex relationships across different entity types and query contexts, (2) adaptively selecting relevant knowledge subgraphs based on query semantics rather than uniformly processing entire network structures, and (3) transforming structural knowledge into coherent natural language explanations that can justify answers across different academic qa scenarios. while these challenges manifest differently across different tasks, they share the common requirement of integrating graph-structured knowledge with semantic reasoning. current approaches to academic question answering have attempted to address these challenges through various strategies. however, existing methods face significant limitations in addressing these challenges holistically. heterogeneous graph neural networks (hgnns) can effectively model complex academic networks @xcite , but struggle with: (1) adapting their representations to different query types and relationship patterns, (2) generating task-specific subgraph selections, and (3) producing natural language explanations for diverse academic qa scenarios. llms demonstrate strong semantic understanding @xcite but cannot directly process the rich structural information embedded in academic networks. existing integration attempts typically focus on single tasks or treat graph information as auxiliary features through simple concatenation, failing to systematically incorporate structural patterns into the reasoning process across diverse academic qa scenarios @xcite . to address these limitations, we propose het-gcot (heterogeneous graph-enhanced chainof-thought), a framework that integrates heterogeneous graph neural networks with large language models for academic question answering. hetgcot transforms graph structural patterns into confidence-weighted natural language reason-ing chains through metapath naturalization, enabling llms to process complex academic relationships. the framework employs adaptive metapath selection using heterogeneous graph transformer (hgt) @xcite embeddings and fastgtn-learned @xcite importance weights to dynamically identify task-relevant subgraphs. through a multistep chain-of-thought reasoning process, hetgcot anchors on three taskdriven analytical foci: analyzing venue patterns for journal recommendation, temporal relationships for authorship queries, and collaboration networks for collaboration discovery. this integrated approach enables deep reasoning-level fusion of graph structures with language understanding across diverse academic qa scenarios. through extensive experiments on openalex and dblp datasets @xcite , we demonstrate hetgcot's effectiveness across multiple academic qa tasks. for journal recommendation, our framework achieves 92.21% and 83.70% h@1 accuracy respectively. moreover, we validate its generalizability on historical publication qa (author-paper reasoning) and author collaboration qa (author-paper-author reasoning), showing consistent improvements on general academic qa tasks. the key contributions of this work include: ## methodology in this section, we present our proposed hetgcot framework. figure 1 illustrates the system architecture designed to address academic question answering through the integration of heterogeneous graph structural information with llm reasoning capabilities. we consider a heterogeneous academic graph @xmath0, e, ϕ, ψ), where @xmath1, authors (v a ), and venues (v v ). e denotes the set of edges @xmath2, capturing paper-venue and paper-author relationships, with ϕ : @xmath3: @xmath4. given a query q (which could be a paper, author, or research topic), our task is to provide accurate answers with interpretable explanations. ## conclusion in this work, we proposed hetgcot, a framework that integrates heterogeneous graph neural networks with large language models for academic question answering. our framework introduces three main contributions: (1) a unified framework that transforms heterogeneous graph structural information into natural language reasoning chains, (2) an adaptive metapath selection mechanism that identifies relevant subgraphs for academic queries, and (3) a multi-step reasoning strategy that incorporates graph-derived contexts into chain-of-thought prompting. through comprehensive experiments on openalex and dblp datasets, we demonstrated that hetgcot significantly outperforms baseline methods. we also validated the framework's adaptability across different llm architectures. future work could extend this approach to more complex academic reasoning tasks, incorporate additional relationship types in scholarly networks, and scale to larger interdisciplinary datasets. the combination of structural graph information with language model reasoning presents promising directions for academic information processing systems.