Agent-Based Dynamic Network Models: Validation on Empirical Data

Inspecting the dynamics of networks opens a new dimension in understanding the mechanisms behind complex social phenomena. In our previous work, we defined a set of elementary dynamic models based on classic random and preferential networks. Focusing on edge dynamics, we defined several processes for changing networks with a fixed set of vertices. We applied simple rules, including the combination of random, preferential and assortative mixing of existing edges. Starting from an empty initial network, we examined network properties (like density, clustering, average path length, number of components and degree distribution) of both snapshot and cumulative networks for various lengths of aggregation time windows. In this paper, we extend our analysis with a comparison to results obtained from empirical data from two selected data sets. The knowledge of the baseline behavior of the abstract elementary dynamic network models helps us to identify the important, idiosyncratic properties of the empirical networks.