Twitter (ICWSM)

This is the directed network containing information about who follows whom on Twitter. Nodes represent users and an edge shows that the left user follows the right one.

Metadata

CodeWs
Internal namemunmun_twitter_social
NameTwitter (ICWSM)
Data sourcehttp://www.public.asu.edu/~mdechoud/datasets.html
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Online social network
Node meaningUser
Edge meaningFollow
Network formatUnipartite, directed
Edge typeUnweighted, no multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsDoes not contain loops

Statistics

Size n =465,017
Volume m =834,797
Wedge count s =187,988,707
Claw count z =28,887,087,190
Cross count x =3,417,980,886,457
Triangle count t =38,389
Square count q =21,828,900
4-Tour count T4 =928,253,108
Maximum degree dmax =678
Maximum outdegree d+max =500
Maximum indegree dmax =199
Average degree d =3.590 39
Fill p =3.860 51 × 10−6
Size of LCC N =465,017
Size of LSCC Ns =1,726
Relative size of LSCC Nrs =0.003 711 69
Diameter δ =8
50-Percentile effective diameter δ0.5 =4.065 65
90-Percentile effective diameter δ0.9 =4.962 07
Mean distance δm =4.594 85
Gini coefficient G =0.694 849
Relative edge distribution entropy Her =0.822 520
Power law exponent γ =4.358 34
Tail power law exponent γt =2.471 00
Tail power law exponent with p γ3 =2.471 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =1.361 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =2.551 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.877 715
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.059 352 6
Clustering coefficient c =0.000 612 627
Spectral norm α =81.599 7
Operator 2-norm ν =79.115 0
Cyclic eigenvalue π =6.178 14
Algebraic connectivity a =0.007 317 00
Spectral separation 1[A] / λ2[A]| =1.050 05
Reciprocity y =0.003 011 51
Non-bipartivity bA =0.046 055 0
Normalized non-bipartivity bN =0.003 658 47
Spectral bipartite frustration bK =0.000 509 603
Controllability C =462,515

Plots

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

Zipf plot

Hop distribution

In/outdegree scatter plot

Clustering coefficient 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] Munmun De Choudhury, Yu-Ru Lin, Hari Sundaram, K. Selçuk Candan, Lexing Xie, and Aisling Kelliher. How does the data sampling strategy impact the discovery of information diffusion in social media? In ICWSM, pages 34–41, 2010.