Fashion knockoff network. Nodes are brands and there is a
directed edge A B if A knocked off a design from B. Thicker arrows indicate
multiple knockoffs. Node size is proportional to the number of incoming
edges, while text size to that of outgoing ones. The color of nodes is
proportional to the HITS authority score of the node, indicating influential
brands. Red-orange nodes have high authority, yellow intermediate, and
green-blue low. For visualization purposes, only brands participating in at
least two knockoffs (received or performed) are displayed.
Fashion knockoff network. Nodes are brands and there is a
directed edge A B if A knocked off a design from B. Thicker arrows indicate
multiple knockoffs. Node size is proportional to the number of incoming
edges, while text size to that of outgoing ones. The color of nodes is
proportional to the HITS authority score of the node, indicating influential
brands. Red-orange nodes have high authority, yellow intermediate, and
green-blue low. For visualization purposes, only brands participating in at
least two knockoffs (received or performed) are displayed.
Abstract
Knowledge discovery techniques have a long history of application to fields of practice such as marketing and business intelligence. Fashion and other manufacturing compartments have comparably enjoyed little attention from computer scientists. With the increasing availability of multimedia data from the Web and social media, our understanding of the fashion apparel industry could be significantly enhanced through the use of knowledge discovery methods and of large scale datasets obtained from places such as Twitter and Instagram. Here, we are interested in one of the issues at the center of the contemporary structure and dynamics of the fashion industry: the practice of knockoffs. We combine Web scraping and network science techniques to give a preliminary characterization of how brands knock designs off each other. Such a study could be one of the first examples of an emergent field, which we refer to and define as “fashion informatics.”