mlsanityData to 14 Oct 2026

Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains

arXiv 2412.08937 ↗First posted 12 Dec 2024cs.LG cs.CLcited by 5

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Abstract (arXiv)

Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespread popularity and application across various domains. However, current research on text-attributed graph learning predominantly focuses on homogeneous graphs, which feature a single node and edge type, thus leaving a gap in understanding how methods perform on HTAGs. One crucial reason is the lack of comprehensive HTAG datasets that offer original textual content and span multiple domains of varying sizes. To this end, we introduce a collection of challenging and diverse benchmark datasets for realistic and reproducible evaluation of machine learning models on HTAGs. Our HTAG datasets are multi-scale, span years in duration, and cover a wide range of domains, including movie, community question answering, academic, literature, and patent networks. We further conduct benchmark experiments on these datasets with various graph neural networks. All source data, dataset construction codes, processed HTAGs, data loaders, benchmark codes, and evaluation setup are publicly available at GitHub and Hugging Face.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 39 of its 48 references that are in the map. First posted on arXiv 12 Dec 2024.

Builds on

39 of 48 references.

References · 39

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27 more
  1. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223
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  3. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  4. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 155
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learningcited by 1
  2. 2025H2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphscited by 0
  3. 2026Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compressioncited by 0
  4. 2026Text-attributed Graph Condensation via Text Selection and Attribute Matchingcited by 0
  5. 2026THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Modelcited by 0