  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.
