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Table 1 Summary of the data manipulation and the desired effect on the graph data

From: The interplay between communities and homophily in semi-supervised classification using graph neural networks

Manipulation Homophily Mixing Side-effects
\(Hom^+\) Increased Preserved Binomial degree distribution
\(Hom^-\) Decreased Increased Destroys community structure
\(Mix^-\) Preserved(*) Decreased Binomial degree distribution
\(Mix^+\) Preserved Increased Destroys sub-communities
  1. (*)\(Mix^-\) should result in a “homophilic” graph if the original was “homophilic”, and vice versa