This work introduces a new approach in solving the cold-start problem in networked services. By incorporating social network information, we demonstrate the ability to get improved forecasts regarding future customer behavior immediately after acquiring new customers.

This work investigates the structure and evolution of Venmo, a peer-to-peer payment application. Venmo is a unique social network in the sense that the edges among nodes represent financial transactions among individuals who shared an offline social interaction. We had two important findings. First, the degree distributions do not follow a power-law distribution, confirming previous studies that real-world social networks are rarely scale-free. Second, we examine the "topological" version of the small-world hypothesis and find that Venmo users are separated by a mean of 5.9 steps and a median of 6 steps confirming Milgram's hypothesis.

This is the outcome of our group's participation in the 11th triennial choice symposium. Our group discussed the emerging topic of data ethics and the choices we have as individuals. Our paper explores how data policies drive technological, organizational, and economic decisions made by digital platforms and consumers, and highlights the need for education on digital and data interactions.  

©2020 by Pantelis Loupos.