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摘要: 近年来, 随着大模型在多模态领域的快速发展, 文本属性图学习也迎来了新的范式. 文本属性图中包含图结构与丰富的节点/边文本属性信息, 广泛存在于社交网络、论文引用网络与知识图谱等场景. 然而, 传统图神经网络通常依赖浅层文本嵌入作为初始节点表示, 难以充分捕捉上下文依赖与复杂语义信息, 并在图结构与文本语义的融合上存在天然局限. 为克服这一瓶颈, 近年来研究者开始探索利用大模型强大的语言理解、知识记忆与推理能力来提升文本属性图学习的效果, 推动了该领域的新一轮发展. 本文系统梳理大模型驱动的文本属性图学习研究进展, 围绕图生成、图预测、图推理三大核心任务, 回顾代表性方法与主要范式, 并总结现有研究中常用的数据集与评测基准. 在文本属性图研究中, 当前在图生成、图预测与图推理三大任务中分别面临结构与属性表达效率不足、生成方法匮乏, 评估体系与可扩展性不完善, 以及复杂关系建模与多跳推理受限等挑战. 结合上述问题提出未来发展方向, 期望为后续研究提供系统化参考与整体性视角, 进一步促进大模型与图学习的深度耦合.Abstract: In recent years, the rapid advancement of large models in multimodal learning has introduced new paradigms for text-attributed graph learning. Text-attributed graphs, which combine graph structures with rich textual attribute information on nodes and edges, are widely found in scenarios such as social networks, paper citation networks, and knowledge graphs. However, traditional graph neural networks typically rely on shallow text embeddings as initial node representations, making it difficult to capture contextual dependencies and complex semantic information, and they face inherent limitations in integrating graph structures with textual semantics. To address this bottleneck, recent studies leverage the powerful language understanding, knowledge retention, and reasoning capabilities of large models to enhance text-attributed graph learning, promoting further development in this field. This paper provides a systematic review of recent advances in large model-driven text-attributed graph learning, covering representative methods and major paradigms across three core tasks: Graph generation, graph prediction, and graph reasoning. We also summarize commonly used datasets and evaluation benchmarks. In text-attributed graph research, current work in the three core tasks faces persistent challenges: Inefficient structure-attribute representation and limited graph generation methods, inadequate evaluation frameworks and scalability in graph prediction, and constrained capacities for complex relational modeling and multi-hop reasoning in graph reasoning. Building on these challenges, we outline future research directions to provide a structured reference and holistic perspective, fostering deeper integration of large models and graph learning.
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Key words:
- large models /
- text-attributed graphs /
- graph neural networks /
- large language models
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图 1 大模型驱动的文本属性图学习研究框架
Fig. 1 Research framework of large-model-driven text-attributed graph learning
表 1 三类范式的核心定位与优缺点对比
Table 1 Comparison of core roles and advantages/disadvantages of three paradigms
范式 核心角色定位 优点 缺点 LLM作为
预测器LLM直接作为主要预测器, 通过提示词完成任务 1)无需显式GNN建模, 端到端简单
2)零样本/少样本能力强
3)天然支持跨任务、跨图泛化1)难以高效建模复杂图结构关系
2)推理成本高, 难以规模化
3)预测不稳定, 易受提示词和上下文长度影响LLM作为
增强器LLM用于增强节点语义, 最终预测由GNN完成 1)充分保留GNN的结构建模优势
2)推理阶段无需或极少依赖LLM, 效率高、成本低
3)更适合大规模图数据与工业场景
4)结构归纳偏置明确, 预测稳定性强1) LLM能力仅作为“辅助”, 未充分发挥其推理能力
2)若协同训练不足, 语义与结构对齐仍可能受限LLM作为
对齐器LLM用于对齐文本、结构、标签等多模态语义空间 1)可统一不同图、不同任务的表示空间
2)提升跨图、跨域泛化能力
3)有利于构建通用预训练模型1)训练流程复杂, 通常需要大规模预训练
2)对数据多样性和设计假设依赖性较强
3)对资源和工程能力要求高
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表 2 经典图推理方法对比
Table 2 Comparison of classical graph reasoning methods
方法 指令微调 推理路径 代码增强 图编码方法 NLGraph[75] × × × 自然语言描述 GPT4Graph[41] × × × 自然语言描述 LLM4DyG[49] × × × 自然语言描述 Talk like a graph[76] × × × 多种自然语言描述 GraphAgent-Reasoner[79] × √ × 自然语言描述 PSEUDO[78] × √ × 伪代码增强 GraphLLM[80] √ × × GNN编码(graph token) GraphToken[81] √ × × GNN编码(soft token) GraphWiz[82] √ √ × 自然语言描述 GUNDAM[83] √ √ × 自然语言描述 GITA[84] √ × × 多模态(视觉+文本) GraphTeam[85] × × √ 自然语言描述 GCoder[86] √ × √ 自然语言描述 PIE[87] × × √ 自然语言描述 CodeGraph[88] × × √ 自然语言描述
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表 3 基于语言模型的图生成常用数据集
Table 3 Common datasets in graph generation models based on language models
数据集 节点数 边数 领域 节点类型 边类型 文献 WARRIOR[205] 100.0 K 285.0 K 社交 原始文本 原始文本 [205] IMDB-text[204] 125.714 K 1.5 M 社交 原始文本 原始文本 [204] Cora-text[10] 48.8 K 110.8 K 社交 原始文本 原始文本 [37, 204] WeiboTech[204] 20.767 K 109.3 K 社交 原始文本 原始文本 [204, 206] WeiboDaily[204] 66.5 K 354.1 K 社交 原始文本 原始文本 [204, 206] Metoo[164] 1 K 32.0 K 社交 原始文本 无 [164, 207] Roe[208] 1 K 121.5 K 社交 原始文本 原始文本 [164, 207] MovieLens-1M[209] 6.0 K/3.9 K 32.0 M 推荐 原始文本 原始文本 [176, 178, 210−211] Amazon review[212] 54.5 M/48.2 M 571.54 M 推荐 原始文本 原始文本 [178, 211, 213 ] Steam[214] 2.6 M/15.5 K 7.793 M 推荐 原始文本 原始文本 [178, 210] Lastfm[215] 1.9 K/17.6 K 266.0 K 推荐 原始文本 无 [216−218]
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表 4 常用图预测数据集统计信息
Table 4 Statistics of commonly used graph prediction datasets
数据集 节点数 边数 类别数 任务类型 领域 特征类型 Cora[227] 2.7 K 5.4 K 7 节点分类 学术 词袋(BoW) Citeseer[227] 3.3 K 4.7 K 6 节点分类 学术 词袋(BoW) Pubmed[227] 19.7 K 44.3 K 3 节点分类 学术 词袋(BoW) Coauthor-CS[227] 18.3 K 81.9 K 15 节点分类 学术 词袋(BoW) Coauthor-Physics[227] 34.5 K 248 K 5 节点分类 学术 词袋(BoW) Amazon-Photo[227] 7.5 K 119 K 10 节点分类 电商 词袋(BoW) Amazon-Computers[227] 13.4 K 245.8 K 8 节点分类 电商 词袋(BoW) ogbn-Product[9] 54 K 74.4 K 47 节点分类 电商 词袋(BoW) WikiCS[227] 11.7 K 216.1 K 10 节点分类 Wikipedia GloVe ogbn-Arxiv-TA[227] 169.3 K 1.2 M 40 节点分类 学术 原始文本 Books-Children[227] 76.9 K 1.6 M 24 节点分类 电商 原始文本 Books-History[227] 41.6 K 358.6 K 12 节点分类 电商 原始文本 Electronics-Computers[227] 87.2 K 721.1 K 10 节点分类 电商 原始文本 Electronics-Photo[227] 48.3 K 500.9 K 12 节点分类 电商 原始文本 Sports-Fitness[227] 173.1 K 1.8 M 13 节点分类 电商 原始文本 CitationV8[227] 1.1 M 6.1 M — 链接预测 学术 原始文本 GoodReads[227] 676.1 K 8.6 M — 链接预测 图书 原始文本 MAG-Mathematics[48, 60] 19.9 K 34.7 K — 链接预测 学术 原始文本 MAG-Geology[48, 60] 20.5 K 51.5 K — 链接预测 学术 原始文本 Reddit[8] 33.4 K 198.4 K 2 节点分类 社交 原始文本 Instagram[8] 11.3 K 144 K 2 节点分类 社交 原始文本 Stackoverflow[58] 129.3 K 281.7 K — 链接预测 技术社区 原始文本 Twitter[16] 176.3 K 2.4 M 2 节点分类, 链接预测 社交 原始文本
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表 5 知识图谱推理数据集
Table 5 Knowledge graph reasoning datasets
数据集 知识图谱源 问题数 多跳 多实体 年份 WebQuestions[228] Freebase 5810 √ × 2013 SimpleQuestions[229] Freebase 108442 × × 2015 ComplexQuestions[230] Freebase 2100 √ √ 2016 GraphQuestions[231] Freebase 5166 √ √ 2016 WebQuestionsSP[232] Freebase 4737 √ × 2016 The 30M Factoid QA[233] Freebase 30 M × × 2016 SimpleQuestionsWikidata[234] Wikidata 21957 × × 2017 LC-QuAD 1.0[235] DBpedia 5000 √ √ 2017 ComplexWebQuestions[236] Freebase 34689 √ √ 2018 QALD-9[237] DBpedia 558 √ √ 2018 PathQuestion[238] Freebase 7106 √ × 2018 MetaQA[239] WikiMovies 407513 √ × 2018 SimpleDBpediaQA[240] DBpedia 43086 × × 2018 LC-QuAD 2.0[241] Wikidata 30000 √ √ 2019 FreebaseQA[242] Freebase 28348 × × 2019 Event-QA[243] EventKG 1000 √ √ 2020 GrailQA[244] Freebase 64331 √ √ 2021
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