  The complementarity and substitutability between products are essential
concepts in retail and marketing. Qualitatively, two products are said to be
substitutable if a customer can replace one product by the other, while they
are complementary if they tend to be bought together. In this article, we take
a network perspective to help automatically identify complements and
substitutes from sales transaction data. Starting from a bipartite
product-purchase network representation, with both transaction nodes and
product nodes, we develop appropriate null models to infer significant
relations, either complements or substitutes, between products, and design
measures based on random walks to quantify their importance. The resulting
unipartite networks between products are then analysed with community detection
methods, in order to find groups of similar products for the different types of
relationships. The results are validated by combining observations from a
real-world basket dataset with the existing product hierarchy, as well as a
large-scale flavour compound and recipe dataset.

Social and Information Networks;Econometrics;Physics and Society