Wikipedia conflict

The edges in this network represent positive and negative conflicts between users of the English Wikipedia, for example users involved in an edit-war. A node represents a user and an edge represents a conflict between two users, with the edge sign representing positive and negative interactions. An example for a negative interaction would be when one user revert the edit of another user.


Internal namewikiconflict
NameWikipedia conflict
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Online contact network
Node meaningUser
Edge meaningEdit conflict
Network formatUnipartite, undirected
Edge typeSigned, possibly weighted, multiple edges
Temporal data Edges are annotated with timestamps
LoopsDoes not contain loops
Zero weights Edges may have weight zero


Size n =118,100
Volume m =2,917,785
Unique edge count m̿ =2,014,062
Loop count l =0
Wedge count s =1,394,925,925
Claw count z =3,157,737,873,756
Triangle count t =13,852,230
Square count q =7,362,075,281
4-Tour count T4 =64,440,643,864
Maximum degree dmax =136,192
Average degree d =49.412 1
Average edge multiplicity m̃ =1.448 71
Size of LCC N =113,123
Diameter δ =10
50-Percentile effective diameter δ0.5 =2.772 34
90-Percentile effective diameter δ0.9 =3.892 43
Median distance δM =3
Mean distance δm =3.363 59
Gini coefficient G =0.809 687
Balanced inequality ratio P =0.167 106
Relative edge distribution entropy Her =0.864 542
Power law exponent γ =1.534 62
Tail power law exponent γt =1.501 00
Degree assortativity ρ =−0.065 011 7
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.029 791 3
Spectral norm α =4,810.78
Algebraic connectivity a =0.018 347 8
Spectral separation 1[A] / λ2[A]| =1.059 93
Non-bipartivity bA =0.260 217
Normalized non-bipartivity bN =0.074 872 1
Algebraic non-bipartivity χ =0.120 114
Spectral bipartite frustration bK =0.000 836 416
Negativity ζ =0.621 160
Triadic conflict τ =0.376 808
Controllability C =43,533
Relative controllability Cr =0.375 839


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

Item rating evolution

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Diameter/density evolution

Signed temporal distribution

Rating class evolution


Inter-event distribution

Node-level inter-event distribution

Matrix decompositions plots



[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]
[2] Ulrik Brandes, Patrick Kenis, Jürgen Lerner, and Denise van Raaij. Network analysis of collaboration structure in Wikipedia. In Proc. Int. World Wide Web Conf., pages 731–740, 2009.