SlowGuess/NeurIPS_2023_OCR huggingface dataset:
As can be observed, our condensed graph enables the GNN model to achieve comparable results with many graph node clustering baseline methods, even though we do not customize the optimization objective targeting node clustering task in the condensation process. These results could justify that: (1) the condensed graph-free data that is synthesized based on GNN classification experts, could also work well in other tasks, even without task-specific customization in the condensation process; (2) the condensed graph-free data contains adequate information about the original large-scale graph, which can be taken as the representative and informative substitution of the original large-scale graph, reflecting the good performance of our proposed method in graph condensation.
E.5 Visualization of Our Condensed Graph-free Data
we present t-SNE [55] plots of the condensed graph-free data of our proposed SFGC under the minimum condensation ratios over all datasets in Fig. A4. The condensed graph-free data shows a well-clustered pattern over Cora and Citeseer. In contrast, on larger-scale datasets with larger condensation ratios, we can also observe some implicit clusters within the same class. These results show that the small-scale graph-free data synthesized by our method has discriminative and representative node attributes that capture comprehensive information from large-scale graphs.