Graph Representation Learning for Inferring Market Structure

Music City Center 

This paper aims to uncover market structure, with a focus on complementary and substitutable relationships, within a large set of products. While understanding market structure has played a crucial role in designing new products, repositioning existing products, and planning marketing actions such as pricing, extant literature has mostly focused on learning market structure for a small subset of products or at an aggregated level (e.g., brand, category). We seek to overcome this limitation by using a modern graph representation learning technique termed Variational Graph Auto Encoder (VGAE). Specifically, we extend VGAE, which has primarily been used to learn synergistic and antagonistic effects among a large set of molecules in the field of Computational Biology, to learn complementary and substitutable relationships among a large set of products.

Market structure

Graph representation learning