Wikipedia talk (it)

This is the communication network of the Italian Wikipedia. Nodes represent users, and an edge from user A to user B denotes that user A wrote a message on the talk page of user B at a certain timestamp.

Metadata

CodeTit
Internal namewiki_talk_it
NameWikipedia talk (it)
Data sourcehttps://zenodo.org/record/49561
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Communication network
Dataset timestamp 2017-10-27
Node meaningUser
Edge meaningMessage
Network formatUnipartite, directed
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsContains loops

Statistics

Size n =863,846
Volume m =3,067,680
Unique edge count m̿ =1,661,453
Loop count l =233,216
Wedge count s =111,830,205,348
Claw count z =11,983,312,889,964,536
Cross count x =1.065 98 × 1021
Triangle count t =3,355,399
Square count q =6,154,887,250
4-Tour count T4 =496,562,923,582
Maximum degree dmax =388,889
Maximum outdegree d+max =388,882
Maximum indegree dmax =15,811
Average degree d =7.102 38
Fill p =2.226 46 × 10−6
Average edge multiplicity m̃ =1.846 38
Size of LCC N =862,214
Size of LSCC Ns =36,356
Relative size of LSCC Nrs =0.042 086 2
Diameter δ =7
50-Percentile effective diameter δ0.5 =2.554 95
90-Percentile effective diameter δ0.9 =3.670 68
Median distance δM =3
Mean distance δm =3.049 93
Gini coefficient G =0.832 891
Balanced inequality ratio P =0.149 587
Outdegree balanced inequality ratio P+ =0.064 084 9
Indegree balanced inequality ratio P =0.232 251
Relative edge distribution entropy Her =0.681 910
Power law exponent γ =4.229 38
Tail power law exponent γt =1.791 00
Degree assortativity ρ =−0.301 420
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.694 897
Clustering coefficient c =9.001 32 × 10−5
Directed clustering coefficient c± =0.030 690 5
Spectral norm α =9,318.44
Operator 2-norm ν =4,750.84
Cyclic eigenvalue π =4,599.85
Algebraic connectivity a =0.102 767
Spectral separation 1[A] / λ2[A]| =1.103 69
Reciprocity y =0.174 273
Non-bipartivity bA =0.712 796
Normalized non-bipartivity bN =0.029 882 2
Spectral bipartite frustration bK =0.004 722 98
Controllability C =831,096
Relative controllability Cr =0.962 088

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

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Diameter/density evolution

SynGraphy

Inter-event distribution

Node-level inter-event distribution

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] Jun Sun, Jérôme Kunegis, and Steffen Staab. Predicting user roles in social networks using transfer learning with feature transformation. In Proc. ICDM Workshop on Data Min. in Netw., 2016.