On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
Hussain Hussain,
Tomislav Duricic,
Elisabeth Lex
et al.
Abstract:Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. In this work, we systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs. Following an ablation study on six datasets, we measure the performance of GNNs on the original graphs, and the change in performance in the presence and the absence of community structur… Show more
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