Google+ (NIPS)

This directed network contains Google+ user–user links. A node represents a user, and a directed edge denotes that one user has the other user in his circles.

Metadata

CodeGP
Internal nameego-gplus
NameGoogle+ (NIPS)
Data sourcehttp://snap.stanford.edu/data/egonets-Gplus.html
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Online social network
Dataset timestamp 2012
Node meaningUser
Edge meaningFriendship
Network formatUnipartite, directed
Edge typeUnweighted, no multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsDoes not contain loops

Statistics

Size n =23,628
Volume m =39,242
Loop count l =0
Wedge count s =14,738,751
Claw count z =6,512,607,606
Cross count x =3,210,181,194,587
Triangle count t =18,221
Square count q =2,358,832
4-Tour count T4 =77,904,048
Maximum degree dmax =2,771
Maximum outdegree d+max =2,748
Maximum indegree dmax =26
Average degree d =3.321 65
Fill p =7.029 36 × 10−5
Size of LCC N =23,613
Size of LSCC Ns =50
Relative size of LSCC Nrs =0.002 116 13
Diameter δ =8
50-Percentile effective diameter δ0.5 =3.487 59
90-Percentile effective diameter δ0.9 =4.582 91
Median distance δM =4
Mean distance δm =3.951 81
Gini coefficient G =0.659 896
Relative edge distribution entropy Her =0.767 492
Power law exponent γ =3.982 21
Tail power law exponent γt =2.621 00
Degree assortativity ρ =−0.388 516
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.056 484 8
Clustering coefficient c =0.003 708 79
Directed clustering coefficient c± =0.115 634
Spectral norm α =63.595 4
Operator 2-norm ν =59.579 0
Cyclic eigenvalue π =6.168 89
Algebraic connectivity a =0.011 436 9
Spectral separation 1[A] / λ2[A]| =1.210 22
Reciprocity y =0.002 446 36
Non-bipartivity bA =0.109 267
Normalized non-bipartivity bN =0.005 820 67
Algebraic non-bipartivity χ =0.011 413 1
Spectral bipartite frustration bK =0.000 859 765
Controllability C =102,240
Relative controllability Cr =0.998 720

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

Delaunay graph drawing

In/outdegree scatter plot

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] Julian McAuley and Jure Leskovec. Learning to discover social circles in ego networks. In Adv. in Neural Inf. Process. Syst., pages 548–556. 2012.