Fig. 9
From: Complex network effects on the robustness of graph convolutional networks
Classification accuracy as a function of heterophilicity using 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 5 train/validation/test splits, and error bars are standard errors. Higher is better for the defender. The principal performance differences occur with skewed degree distributions when homophily is low