TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
<|reference_start|>TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations: Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum of real-world scenarios. Despite the potential for deeper insights, existing TAG representation learning primarily relies on supervised methods, necessitating extensive labeled data and limiting applicability across diverse contexts. This paper introduces a new self-supervised learning framework, Text-And-Graph Multi-View Alignment (TAGA), which overcomes these constraints by integrating TAGs' structural and semantic dimensions. TAGA constructs two complementary views: Text-of-Graph view, which organizes node texts into structured documents based on graph topology, and the Graph-of-Text view, which converts textual nodes and connections into graph data. By aligning representations from both views, TAGA captures joint textual and structural information. In addition, a novel structure-preserving random walk algorithm is proposed for efficient training on large-sized TAGs. Our framework demonstrates strong performance in zero-shot and few-shot scenarios across eight real-world datasets.<|reference_end|>
@article{zhang2024taga:,
    title={TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing
  Graph and Text Mutual Transformations},
    author={Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling, Liang Zhao},
    journal={arXiv preprint arXiv:2405.16800},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16800},
    primaryClass={cs.LG cs.AI}
}