Douban

This is the social network of Douban, a Chinese online recommendation site. The network is undirected and unweighted.

Metadata

CodeDB
Internal namedouban
NameDouban
Data sourcehttp://socialcomputing.asu.edu/datasets/Douban
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Online social network
Node meaningUser
Edge meaningFriendship
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops

Statistics

Size n =154,908
Volume m =327,162
Loop count l =0
Wedge count s =11,745,116
Claw count z =269,719,710
Cross count x =6,564,312,495
Triangle count t =40,612
Square count q =750,163
4-Tour count T4 =53,636,092
Maximum degree dmax =287
Average degree d =4.223 95
Fill p =2.726 77 × 10−5
Size of LCC N =154,908
Diameter δ =9
50-Percentile effective diameter δ0.5 =4.602 48
90-Percentile effective diameter δ0.9 =5.695 94
Median distance δM =5
Mean distance δm =5.100 46
Gini coefficient G =0.694 496
Balanced inequality ratio P =0.214 079
Relative edge distribution entropy Her =0.889 699
Power law exponent γ =2.970 61
Tail power law exponent γt =2.081 00
Tail power law exponent with p γ3 =2.081 00
p-value p =0.000 00
Degree assortativity ρ =−0.180 331
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.010 373 3
Spectral norm α =41.968 2
Algebraic connectivity a =0.018 767 2
Spectral separation 1[A] / λ2[A]| =1.373 38
Non-bipartivity bA =0.395 394
Normalized non-bipartivity bN =0.009 618 19
Algebraic non-bipartivity χ =0.018 791 7
Spectral bipartite frustration bK =0.001 112 21
Controllability C =137,540
Relative controllability Cr =0.887 882

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] R. Zafarani and H. Liu. Social computing data repository at ASU, 2009. [ http ]