In this paper we study how the network of agents adopting a particular
technology relates to the structure of the underlying network over which the
technology adoption spreads. We develop a model and show that the network of
agents adopting a particular technology may have characteristics that differ
significantly from the social network of agents over which the technology
spreads. For example, the network induced by a cascade may have a heavy-tailed
degree distribution even if the original network does not.
This provides evidence that online social networks created by technology
adoption over an underlying social network may look fundamentally different
from social networks and indicates that using data from many online social
networks may mislead us if we try to use it to directly infer the structure of
social networks. Our results provide an alternate explanation for certain
properties repeatedly observed in data sets, for example: heavy-tailed degree
distribution, network densification, shrinking diameter, and network community
profile. These properties could be caused by a sort of `sampling bias' rather
than by attributes of the underlying social structure. By generating networks
using cascades over traditional network models that do not themselves contain
these properties, we can nevertheless reliably produce networks that contain
all these properties.
An opportunity for interesting future research is developing new methods that
correctly infer underlying network structure from data about a network that is
generated via a cascade spread over the underlying network.Comment: To Appear in Proceedings of the 22nd International World Wide Web
Conference(WWW 2013