OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation

• URL: http://arxiv.org/abs/2607.19108v1
• Date: Tue, 21 Jul 2026 13:50:24 GMT
• Title: OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation
• Abstract summary: We present OpenRTAG, a robustness benchmark for text-attributed graph learning.<n>OpenRTAG organizes TAG quality issues into a unified 3 * 3 taxonomy.<n>It supports standardized evaluation across nine TAG datasets and three downstream tasks.
• Score: 22.874435465040488
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Text-attributed graphs (TAGs) are an important graph data form that combine relational structure with rich node text. However, real-world TAGs are often imperfect, with quality issues arising from text, structure, and labels, and typically manifesting as sparsity, noise, and imbalance. These dimensions define nine representative degradation scenarios that can substantially affect TAG learning. Although prior studies have explored specific mitigation strategies, existing evidence remains fragmented across degradation types, datasets, tasks, and model families, leaving TAG robustness insufficiently understood. To address this gap, we present OpenRTAG, a robustness benchmark for text-attributed graph learning. OpenRTAG organizes TAG quality issues into a unified 3 * 3 taxonomy and supports standardized evaluation across nine TAG datasets and three downstream tasks. It systematically evaluates scenario validity and model sensitivity, compares traditional GNNs, LLM-GNNs, and a representative GFM, investigates the effectiveness, efficiency, and robustness of scenario-matched baselines, and further examines model behavior under composite degradation scenarios. OpenRTAG provides a standardized testbed for understanding robustness in TAG learning under realistic low-quality settings.

Related papers

• From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection [89.52759572485276]
  ARC is a few-shot generalist GAD method that leverages in-context learning and requires only a few labeled normal samples at inference time.<n> ARC and ARC_zero effectively detect anomalies, exhibit strong generalization ability, and perform efficiently under few-shot and zero-shot settings.
  arXiv  Detail & Related papers  (2026-02-21T10:59:00Z)
• Text-based Aerial-Ground Person Retrieval [55.31140361809554]
  This work introduces Text-based Aerial-Ground Person Retrieval (TAG-PR)<n>It aims to retrieve person images from heterogeneous aerial and ground views with textual descriptions.
  arXiv  Detail & Related papers  (2025-11-11T15:49:04Z)
• Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses [34.0252107920933]
  We introduce a unified and comprehensive framework to evaluate robustness in TAG learning.<n>Our framework evaluates classical GNNs, robust GNNs (RGNNs), and GraphLLMs across ten datasets from four domains.<n>Our work establishes a foundation for future research on TAG security and offers practical solutions for robust TAG learning in adversarial environments.
  arXiv  Detail & Related papers  (2025-10-20T05:57:54Z)
• GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning [25.487795050809503]
  Generative DyTAG Benchmark (GDGB) comprises eight meticulously curated DyTAG datasets with high-quality textual features.<n>Building on GDGB, we define two novel DyTAG generation tasks: Transductive Dynamic Graph Generation (TDGG) and Inductive Dynamic Graph Generation (IDGG)
  arXiv  Detail & Related papers  (2025-07-04T02:55:32Z)
• HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs [38.79574338268996]
  Graph neural networks (GNNs) have demonstrated success in modeling relational data under the assumption of homophily.<n>Many real-world graphs exhibit heterophily, where linked nodes belong to different categories or possess diverse attributes.<n>We introduce the Heterophilic Text-attributed Graph Benchmark (HeTGB), a novel benchmark comprising five real-world heterophilic graph datasets from diverse domains.
  arXiv  Detail & Related papers  (2025-03-05T02:00:32Z)
• Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains [25.61868709829681]
  We introduce a collection of challenging and diverse benchmark datasets for realistic and reproducible evaluation of machine learning models on HTAGs.<n>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.<n>All source data, dataset construction codes, processed HTAGs, data loaders, benchmark codes, and evaluation setup are publicly available at GitHub and Hugging Face.
  arXiv  Detail & Related papers  (2024-12-12T04:58:32Z)
• Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets [51.74296438621836]
  We introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels. The main limitation of scribbles as source for weak supervision is the lack of challenging datasets for scribble segmentation. Scribbles for All provides scribble labels for several popular segmentation datasets and provides an algorithm to automatically generate scribble labels for any dataset with dense annotations.
  arXiv  Detail & Related papers  (2024-08-22T15:29:08Z)
• Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
  GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information. Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
  arXiv  Detail & Related papers  (2024-06-18T13:35:25Z)
• DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs [28.340416573162898]
  Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios. Despite their broad applicability, there is a notable scarcity of benchmark datasets tailored to DyTAGs. We introduce Dynamic Text-attributed Graph Benchmark (DTGB), a collection of large-scale, time-evolving graphs.
  arXiv  Detail & Related papers  (2024-06-17T20:16:12Z)
• Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks [61.23408995934415]
  We propose a novel framework for minimally supervised categorization by learning from the text-rich network. Specifically, we jointly train two modules with different inductive biases -- a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning. Our experiments show that given only three seed documents per category, our framework can achieve an accuracy of about 92%.
  arXiv  Detail & Related papers  (2021-02-23T04:14:34Z)
• Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [53.31911669146451]
  Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks. These graphs are usually incomplete, urging auto-completion of them. graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings. textual encoding approaches, e.g., KG-BERT, resort to graph triple's text and triple-level contextualized representations.
  arXiv  Detail & Related papers  (2020-04-30T13:50:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.