Jester 100

This weighted network contains information about how users rated a total ammount of 100 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 and right nodes are jokes.

Metadata

CodeJ1
Internal namejester1
NameJester 100
Data sourcehttp://eigentaste.berkeley.edu/dataset/
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Rating network
Dataset timestamp 1999 ⋯ 2003
Node meaningUser, joke
Edge meaningRating
Network formatBipartite, undirected
Edge typeRatings, no multiple edges

Statistics

Size n =73,521
Left size n1 =73,421
Right size n2 =100
Volume m =4,136,360
Wedge count s =102,339,759,934
Claw count z =1,907,263,353,344,646
Cross count x =2.893 18 × 1019
Square count q =2,599,984,203,310
4-Tour count T4 =21,209,254,824,380
Maximum degree dmax =73,413
Maximum left degree d1max =100
Maximum right degree d2max =73,413
Average degree d =112.522
Average left degree d1 =56.337 6
Average right degree d2 =41,363.6
Fill p =0.563 376
Size of LCC N =73,521
Diameter δ =4
50-Percentile effective diameter δ0.5 =1.499 62
90-Percentile effective diameter δ0.9 =1.900 42
Median distance δM =2
Mean distance δm =1.999 22
Gini coefficient G =0.645 907
Balanced inequality ratio P =0.269 422
Left balanced inequality ratio P1 =0.385 244
Right balanced inequality ratio P2 =0.404 071
Relative edge distribution entropy Her =0.756 826
Power law exponent γ =1.849 21
Tail power law exponent γt =2.091 00
Degree assortativity ρ =−0.413 704
Degree assortativity p-value pρ =0.000 00
Spectral norm α =3,007.63
Algebraic connectivity a =8.924 26
Spectral separation |λ1[A] / λ2[A]| =1.172 23
Negativity ζ =0.455 631
Algebraic conflict ξ =14.999 6
Spectral signed frustration φ =0.033 325 9

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

Item rating evolution

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