We have carefully instrumented a large portion of the population living in a
university graduate dormitory by giving participants Android smart phones
running our sensing software. In this paper, we propose the novel problem of
predicting mobile application (known as "apps") installation using social
networks and explain its challenge. Modern smart phones, like the ones used in
our study, are able to collect different social networks using built-in
sensors. (e.g. Bluetooth proximity network, call log network, etc) While this
information is accessible to app market makers such as the iPhone AppStore, it
has not yet been studied how app market makers can use these information for
marketing research and strategy development. We develop a simple computational
model to better predict app installation by using a composite network computed
from the different networks sensed by phones. Our model also captures
individual variance and exogenous factors in app adoption. We show the
importance of considering all these factors in predicting app installations,
and we observe the surprising result that app installation is indeed
predictable. We also show that our model achieves the best results compared
with generic approaches: our results are four times better than random guess,
and predict almost 45% of all apps users install with almost 45% precision (F1
score= 0.43)