29 research outputs found
On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic Counterparts
Data-driven analysis of large social networks has attracted a great deal of
research interest. In this paper, we investigate 120 real social networks and
their measurement-calibrated synthetic counterparts generated by four
well-known network models. We investigate the structural properties of the
networks revealing the correlation profiles of graph metrics across various
social domains (friendship networks, communication networks, and collaboration
networks). We find that the correlation patterns differ across domains. We
identify a non-redundant set of metrics to describe social networks. We study
which topological characteristics of real networks the models can or cannot
capture. We find that the goodness-of-fit of the network models depends on the
domains. Furthermore, while 2K and stochastic block models lack the capability
of generating graphs with large diameter and high clustering coefficient at the
same time, they can still be used to mimic social networks relatively
efficiently.Comment: To appear in International Conference on Advances in Social Networks
Analysis and Mining (ASONAM '19), Vancouver, BC, Canad