Pretty Good Privacy

This is the interaction network of users of the Pretty Good Privacy (PGP) algorithm. The network contains only the giant connected component of the network.

Metadata

CodePG
Internal namearenas-pgp
NamePretty Good Privacy
Data sourcehttp://deim.urv.cat/~aarenas/data/welcome.htm
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Online contact network
Node meaningUser
Edge meaningInteraction
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops
Snapshot Is a snapshot and likely to not contain all data
Connectedness Only the largest connected component of the original data is included

Statistics

Size n =10,680
Volume m =24,316
Loop count l =0
Wedge count s =434,797
Claw count z =7,501,208
Cross count x =180,494,388
Triangle count t =54,788
Square count q =1,010,957
4-Tour count T4 =9,875,476
Maximum degree dmax =205
Average degree d =4.553 56
Fill p =0.000 426 403
Size of LCC N =10,680
Diameter δ =24
50-Percentile effective diameter δ0.5 =7.001 33
90-Percentile effective diameter δ0.9 =10.069 9
Median distance δM =8
Mean distance δm =7.652 86
Gini coefficient G =0.591 824
Balanced inequality ratio P =0.269 329
Relative edge distribution entropy Her =0.921 894
Power law exponent γ =2.109 16
Tail power law exponent γt =4.261 00
Tail power law exponent with p γ3 =4.261 00
p-value p =0.576 000
Degree assortativity ρ =+0.238 211
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.378 025
Spectral norm α =42.435 5
Algebraic connectivity a =0.011 160 4
Spectral separation |λ1[A] / λ2[A]| =1.111 38
Non-bipartivity bA =0.716 478
Normalized non-bipartivity bN =0.009 900 92
Algebraic non-bipartivity χ =0.018 345 5
Spectral bipartite frustration bK =0.001 007 21
Controllability C =2,666
Relative controllability Cr =0.249 625

Plots

Fruchterman–Reingold graph drawing

Degree distribution

Cumulative degree distribution

Lorenz curve

Spectral distribution of the adjacency matrix

Spectral distribution of the normalized adjacency matrix

Spectral distribution of the Laplacian

Spectral graph drawing based on the adjacency matrix

Spectral graph drawing based on the Laplacian

Spectral graph drawing based on the normalized adjacency matrix

Degree assortativity

Zipf plot

Hop distribution

Double Laplacian graph drawing

Delaunay graph drawing

Clustering coefficient distribution

Average neighbor degree distribution

SynGraphy

Matrix decompositions plots

Downloads

References

[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]
[2] Marián Boguñá, Romualdo Pastor-Satorras, Albert Díaz-Guilera, and Alex Arenas. Models of social networks based on social distance attachment. Phys. Rev. E, 70(5):056122, 2004.