Netzschleuder
network catalogue, repository and centrifuge
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You may also take a look at the source code.
The networks in this dataset can be loaded directly from graph-tool with:(and likewise for the other networks available.)import graph_tool.all as gt g = gt.collection.ns["amazon_copurchases/302"]
amazon_copurchases — Amazon co-purchasing network (2003)
- Description
Network of items for sale on amazon.com in 2003 and the items they "recommend" (via the "Customers Who Bought This Item Also Bought" feature). If one item is frequently co-purchased with another, then the first item recommends the second.1
- Tags
- Economic Commerce Unweighted
- Citation
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- J. Leskovec, L. Adamic and B. Adamic. "The Dynamics of Viral Marketing." ACM Transactions on the Web (ACM TWEB), 1 (2007), https://doi.org/10.1145/1232722.1232727 [@sci-hub]
- Upstream URL OK
- http://snap.stanford.edu/data/amazon0302.html
- Networks
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Tip: click on the table header to sort the list. Hover your mouse over it to obtain a legend.
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv 302 262,111 1,234,877 4.71 5.92 17.80 3944.33 -0.00 0.31 38 1.00 Directed Unipartite 6.1 MiB 9.4 MiB N/A 7.7 MiB 312 400,727 3,200,440 7.99 15.83 35.03 895.79 -0.02 0.25 20 1.00 Directed Unipartite 10.7 MiB 19.1 MiB N/A 15.7 MiB 505 410,236 3,356,824 8.18 16.30 40.36 1805.09 -0.01 0.25 22 1.00 Directed Unipartite 10.9 MiB 19.4 MiB N/A 16.0 MiB 601 403,394 3,387,388 8.40 16.15 40.31 3656.50 -0.01 0.26 25 1.00 Directed Unipartite 10.9 MiB 19.5 MiB N/A 16.0 MiB - Ridiculograms
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302 312 505 601
