Text-attributed graphs (TAGs) are widely used in many real-world domains, and learning on TAGs requires jointly modeling text semantics and graph structure. A standard approach for modeling TAGs is to combine a language model (LM) and a graph neural network (GNN), but joint training is computationally expensive and difficult to scale. Dataset distillation is a promising way to reduce training costs, but existing methods are not well suited to TAGs because they are typically designed for a single modality or still require repeatedly training expensive LM-GNN models on the full dataset during distillation. To address this, we propose TaLK, an effective dataset distillation method for TAGs that couples an LM with a graph-aware neural tangent kernel. This design enables efficient dataset distillation, avoiding repeated joint training on the full dataset while reflecting both textual and structural information for effective TAG learning. Experiments on multiple TAG benchmarks show that TaLK consistently outperforms existing baselines and achieves up to 97% of full-dataset performance with only 1% synthetic data.

Vision-Language Models (VLMs) perform well on general multimodal tasks, yet applying them to real-world advertisement (ad) evaluation is challenging due to strong brand specificity and limited labeled data. We introduce a new practical task, brand-specific ad ranking, which aims to rank ads for a target brand prior to deployment by modeling brand-specific effectiveness. To this end, we propose ADvisor, which derives explicit brand-aware decision criteria using VLMs, augments limited brand context with ads from similar brands, and applies reflection-based scoring for ranking. Experiments on real-world advertising data from 10 brands, collected through actual ad campaigns, show that ADvisor outperforms strong baselines by up to 7.2%. Further analyses show the generated criteria capture meaningful brand specificity, and ADvisor also performs strongly in online A/B testing. Our code is available at https://github.com/K-Kyungho/ADvisor.