When dealing with problems arising from applications such as customer purchasing interactions, building the network for a dataset comprised of millions of transactions will lead to some computational issues. In this paper, we tackle that computation challenge by using a parallel approach for network representations in presence of massive amount of purchasing data. Numerical simulations using a real-world problem show the advantages of the proposed parallel solution.