Adaptive Skeleton Coarsening for Text-Attributed Graphs

Text-attributed graphs combine relational structure with rich node text, but their large scale and high-dimensional text semantics make downstream graph learning costly. Existing graph compression methods are primarily designed around topology or generic node attributes and typically do not treat pretrained text semantics as a primary signal for compression decisions, limiting their ability to exploit the rich semantics encoded in node text. We present Adaptive Skeleton Coarsening (ASC), a semantic-first framework that uses pretrained text embeddings as the primary compression signal and structural descriptors as complementary signals. A joint encoder maps these signals into a shared assignment space, where ASC adaptively learns sparse soft node-to-anchor assignments primarily through semantic reconstruction, together with an auxiliary graph-level property prediction objective. In downstream evaluation, the compressed graph replaces the original graph during model training. At a compression ratio of r=0.1, ASC achieves the highest mean classification accuracy on all 11 benchmarks, while substantially reducing the peak memory consumption and training time of downstream GCNs on the evaluated graphs. Under extreme compression at r=0.01, ASC still maintains robust and superior classification performance.