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Table 2 For each fixed combination of n and k, we generate three clusters of networks, as described at the beginning of “Experiments” section. Then, for each fixed \(\beta \in \{0.05, 0.1, 0.2, 0.25, 0.3, 0.4\}\), we compute a silhouette score for the three corresponding groups, using the same procedure that we used to obtain the values in Table 1

From: Robustness of preferential-attachment graphs

Effect of \(n, \beta\)

\(k=3\)

\(n=1000\)

\(n=10{,}000\)

\(n=20{,}000\)

(a) Fixed k and varying \(n, \beta\)

 \(\beta =0.05\)

0.498

0.840

0.891

 \(\beta =0.10\)

0.632

0.882

0.919

 \(\beta =0.20\)

0.719

0.910

0.939

 \(\beta =0.25\)

0.720

0.915

0.940

 \(\beta =0.30\)

0.734

0.924

0.944

 \(\beta =0.40\)

0.738

0.930

0.946

Effect of \(k, \beta\)

\(n=10{,}000\)

\(k=3\)

\(k=4\)

\(k=5\)

(b) Fixed n and varying \(k,\beta\)

 \(\beta =0.05\)

0.840

0.665

0.374

 \(\beta =0.10\)

0.882

0.788

0.573

 \(\beta =0.20\)

0.910

0.851

0.717

 \(\beta =0.25\)

0.915

0.871

0.755

 \(\beta =0.30\)

0.924

0.882

0.787

 \(\beta =0.40\)

0.930

0.889

0.832