Relating Real and Synthetic Social Networks Through Centrality Measures

We perform here a comparative study on the behaviour of real and synthetic social networks with respect to a selection of nine centrality measures. Some of them are topology based (degree, closeness, betweenness), while others consider the relevance of the actors within the network (Katz, PageRank) or their ability to spread influence through it (Independent Cascade rank, Linear Threshold Rank). We run different experiments on synthetic social networks, with 1K, 10K, and 100K nodes, generated according to the Gaussian Random partition model, the stochastic block model, the LFR benchmark graph model and hyperbolic geometric graphs model. Some real social networks are also considered, with the aim of discovering how do they relate to the synthetic models in terms of centrality. Apart from usual statistical measures, we perform a correlation analysis between all the nine measures. Our results indicate that, in general, the correlation matrices of the different models scale nicely with size. Moreover, the correlation plots distinguish four categories that classify most of the real networks studied here. Those categories have a clear correspondence with particular configurations of the models for synthetic networks.