Twitter lists

This directed networks contains Twitter user–user following information. A node represents a user. An edge indicates that the user represented by the left node follows the user represented by the right node.

Metadata

CodeTL
Internal nameego-twitter
NameTwitter lists
Data sourcehttp://snap.stanford.edu/data/egonets-Twitter.html
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Online social network
Dataset timestamp 2012
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 =23,370
Volume m =33,101
Loop count l =0
Wedge count s =1,231,177
Claw count z =43,439,459
Cross count x =1,457,090,152
Triangle count t =8,804
Square count q =165,762
4-Tour count T4 =6,316,466
Maximum degree dmax =239
Maximum outdegree d+max =238
Maximum indegree dmax =57
Average degree d =2.832 78
Fill p =6.060 97 × 10−5
Size of LCC N =22,322
Size of LSCC Ns =38
Relative size of LSCC Nrs =0.001 626 02
Diameter δ =15
50-Percentile effective diameter δ0.5 =5.636 00
90-Percentile effective diameter δ0.9 =7.559 40
Median distance δM =6
Mean distance δm =6.188 24
Gini coefficient G =0.613 256
Balanced inequality ratio P =0.259 448
Outdegree balanced inequality ratio P+ =0.311 803
Indegree balanced inequality ratio P =0.409 293
Relative edge distribution entropy Her =0.867 531
Power law exponent γ =4.333 57
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 =2.941 00
Outdegree p-value po =0.001 000 00
Indegree tail power law exponent with p γ3,i =2.841 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.477 982
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =−0.058 215 9
Clustering coefficient c =0.021 452 6
Directed clustering coefficient c± =0.207 821
Spectral norm α =25.209 8
Operator 2-norm ν =20.574 3
Cyclic eigenvalue π =6.316 63
Algebraic connectivity a =0.005 031 58
Spectral separation 1[A] / λ2[A]| =1.271 08
Reciprocity y =0.016 313 7
Non-bipartivity bA =0.335 034
Normalized non-bipartivity bN =0.002 550 68
Algebraic non-bipartivity χ =0.005 056 39
Spectral bipartite frustration bK =0.000 443 346
Controllability C =75,871
Relative controllability Cr =0.987 338

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

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.