Potential networks, contagious communities, and understanding social network structure

By Grant Robert Schoenebeck. 2013

22 citations recorded by the source corpus.

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.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “The social networks (re)constructed from observed patterns in social datasets may be fundamentally diﬀerent from the underlying (oﬄine) networks (Schoenebeck, 2013a), posing threats to the ecological/external validity.”
Identifying Platform Effects in Social Media Data (2016, background): “This provides an empirical extension of a concern raised by Schoenebeck (2013) about how variation in technology adoption creates online social networks that differ systematically from the underlying social network: from our results, we see it is not just the process of joining social networking…”