Fig. 7
From: Complex network effects on the robustness of graph convolutional networks
Robustness to influence attacks using GCNs 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 curve represents the average required budget over 25 randomly selected targets, and error bars are standard errors. Higher is better for the defender. Of the datasets where robustness improves using GreedyCover (i.e., CiteSeer, Cora, and PolBlogs), the only case that consistently performs better than GreedyCover is 30% random selection on the Cora dataset