Amazon (Wang)
This is the rating network from Amazon, where users rate individual items.
Metadata
Statistics
| Size | n = | 26,911
|
| Left size | n1 = | 26,112
|
| Right size | n2 = | 799
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| Volume | m = | 29,062
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| Unique edge count | m̿ = | 28,901
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| Wedge count | s = | 3,466,223
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| Claw count | z = | 506,135,573
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| Cross count | x = | 69,876,374,335
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| Square count | q = | 3,575
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| 4-Tour count | T4 = | 13,952,650
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| Maximum degree | dmax = | 812
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| Maximum left degree | d1max = | 44
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| Maximum right degree | d2max = | 812
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| Average degree | d = | 2.159 86
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| Average left degree | d1 = | 1.112 97
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| Average right degree | d2 = | 36.373 0
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| Fill | p = | 0.001 385 24
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| Average edge multiplicity | m̃ = | 1.005 57
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| Size of LCC | N = | 25,865
|
| Diameter | δ = | 18
|
| 50-Percentile effective diameter | δ0.5 = | 5.321 60
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| 90-Percentile effective diameter | δ0.9 = | 7.354 18
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| Median distance | δM = | 6
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| Mean distance | δm = | 5.679 07
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| Gini coefficient | G = | 0.540 336
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| Balanced inequality ratio | P = | 0.309 436
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| Left balanced inequality ratio | P1 = | 0.474 309
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| Right balanced inequality ratio | P2 = | 0.187 156
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| Relative edge distribution entropy | Her = | 0.828 447
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| Power law exponent | γ = | 9.291 60
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| Tail power law exponent | γt = | 3.311 00
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| Tail power law exponent with p | γ3 = | 3.311 00
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| p-value | p = | 0.000 00
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| Left tail power law exponent with p | γ3,1 = | 4.081 00
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| Left p-value | p1 = | 0.000 00
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| Right tail power law exponent with p | γ3,2 = | 1.811 00
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| Right p-value | p2 = | 0.000 00
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| Degree assortativity | ρ = | −0.039 696 6
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| Degree assortativity p-value | pρ = | 1.469 29 × 10−11
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| Spectral norm | α = | 38.540 8
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| Algebraic connectivity | a = | 0.000 958 870
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| Spectral separation | |λ1[A] / λ2[A]| = | 1.346 72
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| Negativity | ζ = | 0.368 257
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| Algebraic conflict | ξ = | 0.012 657 3
|
| Spectral signed frustration | φ = | 0.001 456 17
|
| Controllability | C = | 25,349
|
| Relative controllability | Cr = | 0.941 957
|
Plots
Matrix decompositions plots
Downloads
References
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[1]
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Jérôme Kunegis.
KONECT – The Koblenz Network Collection.
In Proc. Int. Conf. on World Wide Web Companion, pages
1343–1350, 2013.
[ http ]
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[2]
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Hongning Wang, Yue Lu, and Chengxiang Zhai.
Latent aspect rating analysis on review text data: A rating
regression approach.
In Proc. Int. Conf. on Knowl. Discov. and Data Min., pages
783–792, 2010.
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[3]
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Hongning Wang, Yue Lu, and ChengXiang Zhai.
Latent aspect rating analysis without aspect keyword supervision.
In Proc. Int. Conf. on Knowl. Discov. and Data Min., pages
618–626, 2011.
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