Appendix

This is the appendix of our work: 'Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data'. In this appendix, we provide more details of the proposed SFGC in terms of related works, potential application scenarios, dataset statistics, method analysis, and experimental settings with some additional results. Dataset Distillation (Condensation) aims to synthesize a small typical dataset that distills the most important knowledge from a given large target dataset, such that the synthesized small dataset could serve as an effective substitution of the large target dataset for various scenarios [30, 49], e.g., model training and inference, architecture search, and continue learning. Typically, DD [59] and DC-KRR [39] adopted the meta-learning framework to solve bi-level distillation objectives through calculating meta-gradients. In contrast, DC [77], DM [76], and MTT [4] designed surrogate functions to avoid unrolled optimization through the gradient matching, feature distribution matching, and training trajectory matching, respectively, where the core idea is to effectively mimic the large target dataset in the synthesized small dataset.