FilmTrust ratings
This is bipartite rating network of the FilmTrust project. We removed three
instances of duplicate user–film pairs; making the network simple rather than
having multiple edges.
Metadata
Statistics
Size | n = | 3,579
|
Left size | n1 = | 1,508
|
Right size | n2 = | 2,071
|
Volume | m = | 35,494
|
Wedge count | s = | 9,820,232
|
Claw count | z = | 2,067,481,321
|
Cross count | x = | 376,620,963,773
|
Square count | q = | 99,312,706
|
4-Tour count | T4 = | 833,882,216
|
Maximum degree | dmax = | 1,044
|
Maximum left degree | d1max = | 244
|
Maximum right degree | d2max = | 1,044
|
Average degree | d = | 19.834 6
|
Average left degree | d1 = | 23.537 1
|
Average right degree | d2 = | 17.138 6
|
Fill | p = | 0.011 365 1
|
Size of LCC | N = | 3,574
|
Diameter | δ = | 7
|
50-Percentile effective diameter | δ0.5 = | 2.739 74
|
90-Percentile effective diameter | δ0.9 = | 3.839 54
|
Median distance | δM = | 3
|
Mean distance | δm = | 3.255 39
|
Gini coefficient | G = | 0.716 391
|
Balanced inequality ratio | P = | 0.232 969
|
Left balanced inequality ratio | P1 = | 0.312 194
|
Right balanced inequality ratio | P2 = | 0.116 076
|
Relative edge distribution entropy | Her = | 0.811 239
|
Power law exponent | γ = | 1.605 27
|
Tail power law exponent | γt = | 1.601 00
|
Tail power law exponent with p | γ3 = | 1.601 00
|
p-value | p = | 0.000 00
|
Left tail power law exponent with p | γ3,1 = | 3.181 00
|
Left p-value | p1 = | 0.218 000
|
Right tail power law exponent with p | γ3,2 = | 2.061 00
|
Right p-value | p2 = | 0.000 00
|
Degree assortativity | ρ = | −0.450 722
|
Degree assortativity p-value | pρ = | 0.000 00
|
Spectral norm | α = | 45.347 7
|
Algebraic connectivity | a = | 0.007 217 86
|
Spectral separation | |λ1[A] / λ2[A]| = | 1.343 74
|
Negativity | ζ = | 0.439 736
|
Algebraic conflict | ξ = | 0.267 701
|
Spectral signed frustration | φ = | 0.003 369 74
|
Controllability | C = | 2,478
|
Relative controllability | Cr = | 0.692 372
|
Plots
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]
|
Guobing Guo, Jia Zhang, and Neil Yorke-Smith.
A novel Bayesian similarity measure for recommender systems.
In Proc. Int. Joint Conf. on Artif. Intell., pages 2619–2625,
2013.
|