ogbn_products — Amazon product co-purchases (ogbn-products)
- Description
An undirected network of products sold on Amazon, two products being connected if they were bought together, from the
ogbn-productsdataset of the Open Graph Benchmark (OGB), a benchmark for node classification (version 1, of May 2020). The network was built by W.-L. Chiang and colleagues for the evaluation of Cluster-GCN (where it is called "Amazon2M"), from the Amazon product data of the Extreme Classification Repository (originally collected by J. McAuley and colleagues from the Amazon website), and processed further by the OGB authors.Each product is labeled with one of 47 top-level categories of Amazon (the target of the classification task), and has the node features of the benchmark: a 100-dimensional vector obtained from the bag-of-words of its description, reduced by principal component analysis. The products are identified by their Amazon ASIN; their names and descriptions are not included. The benchmark split of the products into training, validation and test sets follows their sales rank (the top 8% are for training, the next 2% for validation and the rest for testing) and is kept as the property
split.As noted by OGB, a few self-loops appear more than once; they are kept as given.
The network has the following properties:
- (Node)
asin: the Amazon Standard Identification Number of the product. - (Node)
category: the top-level category of the product, e.g.BooksorElectronics(as named in the mapping file of OGB, where one category has an empty name and another one is named#508510). - (Node)
split: the set of the product in the benchmark's split by sales rank:train(the 8% best-selling products),valid(the next 2%), ortest(the rest). - (Node)
features: the 100-dimensional feature vector of the product (the reduced bag-of-words of its description).
- (Node)
- Tags
- Economic Commerce Unweighted Metadata
- Added
- 2026-10-28
- Citation
-
- W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, J. Leskovec, "Open Graph Benchmark: Datasets for Machine Learning on Graphs." Advances in Neural Information Processing Systems 33, 22118-22133 (2020) 10.48550/arXiv.2005.00687 Sci-Hub
- W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, C.-J. Hsieh, "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 257-266 (2019) 10.1145/3292500.3330925 Sci-Hub
- Upstream URL OK
-
https://ogb.stanford.edu/docs/nodeprop/#ogbn-products
https://snap.stanford.edu/ogb/data/ - Upstream license
- Amazon license (academic research use; no resale or republication), https://web.archive.org/web/20230609045700/http://s3.amazonaws.com/amazon-reviews-pds/LICENSE.txt
- Networks
-
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