Shopping basket analysis based on the social network theory

This paper applies the social network theory to analyze the FOODMART sales dataset which is from a large supermarket company in the United States. We first measure the node degree distribution, the average path length and the clustering coefficient. The results show that the basket network accords with the characteristics of a small world network, but its topology is different from a number of actual large social networks. Its point degree distribution follows a Poisson distribution rather than a power-law distribution. We then try to find the cliques in the network and conclude that products which have same attributes connect more closely each other than the products which have different attributes. Furthermore, we also find that family members with similar age structure buy the similar products.