Semi-Supervised Text-Attributed Graph Distillation

• URL: http://arxiv.org/abs/2607.20477v1
• Date: Wed, 27 May 2026 14:52:24 GMT
• Title: Semi-Supervised Text-Attributed Graph Distillation
• Abstract summary: em Text-Attributed Graphs (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics.<n>We propose algo, a unified semi-supervised framework guided by the em Wasserstein Distance (WSD)
• Score: 15.73558394941586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Existing representation learning methods over TAGs suffer from severe scalability bottlenecks, particularly together with {\em Large Language Models} (LLMs). While data distillation offers a promising data-centric solution, existing methods fail to capture the complex interplay between graph and text modalities, struggle with the label scarcity inherent in semi-supervised settings, and lack the ability to produce the human-readable textual attributes required for downstream LLM-based tasks. To address these challenges, we propose \algo{}, a unified semi-supervised framework guided by the {\em Wasserstein Distance} (WSD). Grounded in our empirical findings on real TAGs, \algo{} introduces a graph-text collaborative encoding module that utilizes dual-pathway encoders (graph-aware and -free) within a collaborative self-training scheme to harvest reliable pseudo-labels and fuse complementary graph-text features. Furthermore, we develop a theoretically grounded WSD-based graph sketching algorithm and a cost-effective LLM text synthesis module, which leverages cluster-based keyword extraction to generate coherent, human-readable summaries for condensed nodes. Extensive experiments on benchmark datasets demonstrate that \algo{} achieves a state-of-the-art performance-compression trade-off in terms of both GNN- and LLM-based downstream tasks, enabling effective and efficient TAG learning or analytics.

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