What does Big Data tell? Sampling the social network by communication channels

Abstract

Big Data has become the primary source of understanding the structure and dynamics of the society at large scale. The network of social interactions can be considered as a multiplex, where each layer corresponds to one communication channel and the aggregate of all of them constitutes the entire social network. However, usually one has information only about one of the channels or even a part of it, which should be considered as a subset or sample of the whole. Here we introduce a model based on a natural bilateral communication channel selection mechanism, which for one channel leads to consistent changes in the network properties. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get a monotonously decreasing distribution as observed in empirical studies of single channel data. We also find that assortativity may occur or get strengthened due to the sampling method. We analyze the far-reaching consequences of our findings.

12 pages, 9 figures

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• An open-source job management framework for parameter-space exploration: OACIS
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• Deep learning based parameter search for an agent based social network model
• CARAVAN: a framework for comprehensive simulations on massive parallel machines
• Sampling networks by nodal attributes
• Opinion formation in multiplex networks with general initial distributions
• Homophilic organization of egocentric communities in ICT services