Potential Networks, Contagious Communities, and Understanding Social Network Structure

TL;DR

This paper explores how technology adoption cascades can create observed social network structures that differ from the true underlying network, affecting how we interpret online social data.

Contribution

It introduces a model showing that cascade-induced networks can have different properties than the original social network, challenging assumptions in social network analysis.

Findings

Cascade networks can have heavy-tailed degree distributions.

Observed properties like densification and shrinking diameter may result from sampling bias.

Generated networks can replicate real-world properties without inherent social structure features.

Abstract

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.