Fig. 3
From: Complex network effects on the robustness of graph convolutional networks
Robustness to influence attacks against GCNs on simulated data. Results are shown for ER (first column), BA (second column), WS (third column), and LFR (fourth column) graphs, in cases with no attributes (first row), uninformative attributes (second row), moderately informative attributes (third row), and highly informative attributes (fourth row). Each curve represents the average required budget over 25 randomly selected targets, and error bars are standard errors. Higher is better for the defender. Results are shown for high homophily (solid line) and low homophily (dash line) cases. As attributes become more helpful in classification, the advantage gained by the alternative training methods is substantially reduced