Amazon ratings
This bipartite network contains product ratings from the Amazon online shopping
website. The rating scale ranges from 1 to 5, where 5 denotes the most positive
rating. Nodes represent users and products, and edges represent individual
ratings.
Metadata
Statistics
Size  n =  3,376,972

Left size  n_{1} =  2,146,057

Right size  n_{2} =  1,230,915

Volume  m =  5,838,041

Unique edge count  m̿ =  5,743,258

Wedge count  s =  627,186,651

Claw count  z =  704,564,362,291

Cross count  x =  1,684,620,121,457,183

Square count  q =  35,849,304

4Tour count  T_{4} =  2,807,043,116

Maximum degree  d_{max} =  12,217

Maximum left degree  d_{1max} =  12,217

Maximum right degree  d_{2max} =  3,146

Average degree  d =  3.457 56

Average left degree  d_{1} =  2.720 36

Average right degree  d_{2} =  4.742 85

Fill  p =  2.174 15 × 10^{−6}

Average edge multiplicity  m̃ =  1.016 50

Size of LCC  N =  2,892,456

Diameter  δ =  28

50Percentile effective diameter  δ_{0.5} =  5.924 44

90Percentile effective diameter  δ_{0.9} =  8.044 95

Median distance  δ_{M} =  6

Mean distance  δ_{m} =  6.629 27

Gini coefficient  G =  0.650 684

Balanced inequality ratio  P =  0.248 637

Left balanced inequality ratio  P_{1} =  0.276 822

Right balanced inequality ratio  P_{2} =  0.236 379

Relative edge distribution entropy  H_{er} =  0.916 513

Power law exponent  γ =  2.957 80

Tail power law exponent  γ_{t} =  2.071 00

Degree assortativity  ρ =  −0.035 657 9

Degree assortativity pvalue  p_{ρ} =  0.000 00

Spectral norm  α =  7,942.59

Spectral separation  λ_{1}[A] / λ_{2}[A] =  3.442 97

Negativity  ζ =  0.377 003

Controllability  C =  1,690,624

Relative controllability  C_{r} =  0.500 633

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]

EePeng Lim, VietAn Nguyen, Nitin Jindal, Bing Liu, and Hady Wirawan Lauw.
Detecting product review spammers using rating behaviors.
In Proc. Int. Conf. on Information and Knowl. Management, pages
939–948, 2010.

[3]

Nitin Jindal and Bing Liu.
Opinion spam and analysis.
In Proc. Int. Conf. on Web Search and Web Data Min., pages
219–230, 2008.

[4]

Arjun Mukherjee, Bing Liu, and Natalie Glance.
Spotting fake reviewer groups in consumer reviews.
In Proc. Int. Conf. on World Wide Web, pages 191–200, 2012.
