walmart_trips — Walmart shopping trips (2015)
- Description
A bipartite network of shopping trips at Walmart stores and the products bought in each trip. The purchase data were released by Walmart for the 2015 Kaggle competition "Walmart Recruiting: Trip Type Classification", in which each record is an item scanned during a customer visit (identified by a visit number, with the product's UPC code, its store department and the number of items scanned), and visits were to be classified into trip types defined by Walmart. Amburg, Veldt and Benson formed the trips from these records and assigned each product to one of ten broad departments under which it appears on walmart.com, or to an "Other" class. Each trip in the network contains at least two distinct products; not all visits of the competition data are included, and the selection criteria are not documented. Product identities, item counts and the trip types are not included in this version of the data.
The network has the following properties:
is_trip: whether the node is a shopping trip (otherwise it is a product).- Product nodes:
department, the walmart.com department of the product: "Clothing, Shoes & Accessories", "Electronics and Office", "Home, Furniture & Appliances", "Home Improvement & Patio", "Baby", "Toys, Games, and Video Games", "Food, Household & Pets", "Pharmacy, Health & Beauty", "Sports, Fitness & Outdoors", "Auto, Tires & Industrial", or "Other".
- Tags
- Commerce Offline Unweighted Metadata
- Citation
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- I. Amburg, N. Veldt, A. R. Benson, "Clustering in graphs and hypergraphs with categorical edge labels." Proceedings of The Web Conference 2020, 706-717 (2020) 10.1145/3366423.3380152 Sci-Hub
- Kaggle, "Walmart Recruiting: Trip Type Classification." (2015) www.kaggle.com
- Upstream URL OK
- https://www.cs.cornell.edu/~arb/data/walmart-trips/
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties