2015/03/23 by Isabel M. Kloumann, Isabel Kloumann, Lada A. Adamic +3
Computer Science · Decision Sciences · Physics and Astronomy · #Complex Network Analysis Techniques #Computer science #Computer security #Data science #Ecology #Human–computer interaction #Innovation Diffusion and Forecasting #Internet privacy #Login #Opinion Dynamics and Social Influence #Popularity #Population #Set (abstract data type) #Social media #Social network (sociolinguistics) #Sociality #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1145/2736277.2741684
11 pages, 10 figures, 3 tables, International World Wide Web Conference
arxiv created 2015/03/23 · arxiv updated 2015/03/25 · openalex publication_date 2015/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Apps are emerging as an important form of on-line content, and they combine aspects of Web usage in interesting ways --- they exhibit a rich temporal structure of user adoption and long-term engagement, and they exist in a broader social ecosystem that helps drive these patterns of adoption and engagement. It has been difficult, however, to study apps in their natural setting since this requires a simultaneous analysis of a large set of popular apps and the underlying social network they inhabit. In this work we address this challenge through an analysis of the collection of apps on Facebook Login, developing a novel framework for analyzing both temporal and social properties. At the temporal level, we develop a retention model that represents a user's tendency to return to an app using a very small parameter set. At the social level, we organize the space of apps along two fundamental axes --- popularity and sociality --- and we show how a user's probability of adopting an app depends both on properties of the local network structure and on the match between the user's attributes, his or her friends' attributes, and the dominant attributes within the app's user population. We also devolop models that show the importance of different feature sets with strong performance in predicting app success.