A Social Network Study of the Apple vs. Android Smartphone Battle
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
The Activation of Core Social Networks in the Wake of the 22 July Oslo Bombing
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
| Hi-index | 0.00 |
To understand the diffusive spreading of a product in a telecom network, whether the product is a service, handset, or subscription, it can be very useful to study the structure of the underlying social network. By combining mobile traffic data and product adoption history from one of Telenor’s markets, we can define and measure an adoption network—roughly, the social network of adopters. By studying the time evolution of adoption networks, we can observe how different products diffuses through the network, and measure potential social influence. This paper presents an empirical and comparative study of three adoption networks evolving over time in a large telecom network. We believe that the strongest spreading of adoption takes place in the dense core of the underlying network, and gives rise to a dominant largest connected component (LCC) in the adoption network, which we call “the social network monster”. We believe that the size of the monster is a good indicator for whether or not a product is taking off. We show that the evolution of the LCC, and the size distribution of the other components, vary strongly with different products. The products studied in this article illustrate three distinct cases: that the social network monsters can grow or break down over time, or fail to occur at all. Some of the reasons a product takes off are intrinsic to the product; there are also aspects of the broader social context that can play in. Tentative explanations are offered for these phenomena. Also, we present two statistical tests which give an indication of the strength of the spreading over the social network. We find evidence that the spreading is dependent on the underlying social network, in particular for the early adopters.