Fig. 6
From: The risk of node re-identification in labeled social graphs
Probability distribution of the feature importance scores across original networks. NDD features are presented in the index order of node (N), hop (H) and bin (B). As an example, the feature N1-H2-B1 presents the first bin of the \(NDD^{2}_{1}[k]\) vector. NAD features are presented in the index order of node (N), hop (H) and binary attribute value ∈R,B. As an example, the feature N1-H2-R presents \(NAD^{2}_{1}[R]\). Any feature that does not contribute to the final prediction decision with at least 1% of the samples in average is omitted. a fb-caltech(gender) b fb-caltech(occupation) c polblogs (party) d fb-dartmouth (gender) e fb-dartmouth (occupation) f pokec-1 (gender) g fb-michigan (gender) h fb- michigan (occupation) i amazon-products (category)