Fig. 8
From: Complex network effects on the robustness of graph convolutional networks
Classifier performance across datasets when training data are selected using GreedyCover, StratDegree, or varying amounts of random selection. Results are shown for the CiteSeer (upper left), Cora (upper right), PolBlogs (lower left), and PubMed (lower right) datasets. Each bar height represents the average \(F_1\) score (macro averaged) across 5 separate train/validation/test sets, and error bars are standard errors. Performance is shown for each classifier where experiments completed within the allotted time (24Â h per trial). Higher is better for the defender. While StratDegree often underperforms random selection, GreedyCover typically shows similar performance