Jester 150

This weighted network contains information about how users rated a total amount of 150 jokes. Not every user rated every joke. Rating values are continuous values between −10 and +10. An edge shows that a user has rated a joke. Left nodes are users; right nodes are jokes.


Internal namejester2
NameJester 150
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Rating network
Dataset timestamp 2006-11 ⋯ 2012-11
Node meaningUser, joke
Edge meaningRating
Network formatBipartite, undirected
Edge typeRatings, no multiple edges


Size n =50,832
Left size n1 =50,692
Right size n2 =140
Volume m =1,728,847
Wedge count s =19,185,429,458
Claw count z =220,017,686,996,709
Cross count x =2,405,341,029,254,686,208
Square count q =266,934,299,745
4-Tour count T4 =2,212,223,162,138
Maximum degree dmax =50,692
Maximum left degree d1max =140
Maximum right degree d2max =50,692
Average degree d =68.022 0
Average left degree d1 =34.104 9
Average right degree d2 =12,348.9
Fill p =0.243 607
Size of LCC N =50,832
Diameter δ =3
50-Percentile effective diameter δ0.5 =1.500 45
90-Percentile effective diameter δ0.9 =1.901 77
Median distance δM =2
Mean distance δm =2.000 89
Gini coefficient G =0.737 569
Balanced inequality ratio P =0.214 431
Left balanced inequality ratio P1 =0.312 166
Right balanced inequality ratio P2 =0.354 322
Relative edge distribution entropy Her =0.759 472
Power law exponent γ =1.927 86
Tail power law exponent γt =1.881 00
Degree assortativity ρ =−0.492 637
Degree assortativity p-value pρ =0.000 00
Spectral norm α =1,863.19
Algebraic connectivity a =2.215 21
Negativity ζ =0.445 897
Algebraic conflict ξ =7.650 34
Spectral signed frustration φ =0.028 117 1


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

Item rating evolution

Edge weight/multiplicity 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] Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. Eigentaste: A constant time collaborative filtering algorithm. Inf. Retrieval, 4(2):133–151, 2001.