🧑‍🔬 Xiao Xia Huang

🏛️ Affiliations

🎓Hong Kong Polytechnic UniversityHong Kong · China2020–2026 · 67 papersEmployment since 2020 · Assistant Professor, Computingcurrent🎓Texas A&M UniversityCollege Station · United States2015–2020 · 14 papersEducation 2015–2020 · Ph.D., Computer Science and Engineering🏛️Chinese Academy of Tropical Agricultural SciencesHaikou · China2019–2025 · 7 papers🔬Rubber Research InstituteHaikou · China2019–2025 · 7 papers🎓Technical University of MunichMunich · Germany2010–2018 · 7 papers🎓Hubei University of Chinese MedicineWuhan · China2023–2025 · 4 papers🏛️China National Nuclear CorporationBeijing · China2025 · 2 papers🎓Southwest Jiaotong UniversityChengdu · China2024–2025 · 2 papers🏛️Ministry of Agriculture and Rural AffairsBeijing · China2021–2022 · 2 papers🎓Liaocheng Vocational and Technical CollegeLiaocheng · China2011–2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 18 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models (2025 · 64 citations)
   General-purpose LLMs often fall short in specialized fields. Their domain knowledge is shallow, they struggle with long chains of rule-bound reasoning, and they misread terms whose meaning depends on context. Fine-tuning on domain data can introduce hallucinations and catastrophic forgetting. Classic RAG avoids…
2. Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025 · 9 citations)
   Text-attributed graphs (TAGs) are graphs whose nodes carry text and whose edges encode relations. Learning on them usually follows a two-stage cascade: a language model produces frozen node embeddings, and a GNN is then trained on those embeddings. The authors argue that this split ignores how text and topology…
3. You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures (2025 · 9 citations)
   Graph-based RAG systems such as Microsoft GraphRAG, HippoRAG, RAPTOR, LightRAG and KGP first convert the entire corpus into a graph.
4. Text-Attributed Graph Learning with Coupled Augmentations (2025 · 5 citations)
   Text-attributed graphs are networks whose nodes carry text, such as product descriptions or paper abstracts.
5. LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation (2026 · 1 citation)
   GraphRAG systems answer questions by reasoning over a knowledge graph built from a text corpus. They tend to resist classic RAG attacks for three reasons: injected documents end up isolated in the graph, embedded instructions are lost when text becomes entity–relation structures, and redundant paths compensate for…

All works

9 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

1. A Survey of Graph Retrieval-Augmented Generation for Customized Large Language ModelsarXiv:2501.13958Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, et al.2025 · 64 citations
2. Temporal Augmented Graph Neural Networks for Session-Based Recommendations ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetHuachi Zhou et al.2021 · 61 citations
3. Taming Language Models for Text-attributed Graph Learning with Decoupled AggregationAnnual Meeting of the Association for Computational Linguistics (ACL)Chuang Zhou, Zhu Wang, Shengyuan Chen, et al.2025 · 9 citations
4. You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning StructuresarXiv:2508.06105Shengyuan Chen, Chuang Zhou, Zheng Yuan, et al.2025 · 9 citations
5. Text-Attributed Graph Learning with Coupled AugmentationsInternational Conference on Computational Linguistics (COLING)Chuang Zhou, Jiahe Du, Huachi Zhou, et al.2025 · 5 citations
6. LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZerui Chen et al.2026 · 2 citations
7. LogicPoison: Logical Attacks on Graph Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Yilin Xiao, Jin Chen, Qinggang Zhang, et al.2026 · 1 citation
8. Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetChuang Zhou et al.2026
9. Query-Aware Knowledge Retrieval via Hyperbolic Structuring ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetChuang Zhou et al.2026

🤝 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

• Chuang Zhou ×5
• Hao Chen ×3
• Qinggang Zhang ×3
• Shengyuan Chen ×3
• Yilin Xiao ×3
• Huachi Zhou ×2
• Jiahe Du ×2
• Zheng Yuan ×2
• Xin Yang
• Yi Chang
• Yuanchen Bei
• Zhaozhuo Xu

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL) ×5: ACL 2026, ACL 2025
• International Conference on Computational Linguistics (COLING): COLING 2025
• International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

🔗 Cited by

Papers of our library cite their works 74 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated 31 October 2026

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