  Sparsification reduces the size of networks while preserving structural and
statistical properties of interest. Various sparsifying algorithms have been
proposed in different contexts. We contribute the first systematic conceptual
and experimental comparison of \textit{edge sparsification} methods on a
diverse set of network properties. It is shown that they can be understood as
methods for rating edges by importance and then filtering globally by these
scores. In addition, we propose a new sparsification method (\textit{Local
Degree}) which preserves edges leading to local hub nodes. All methods are
evaluated on a set of 100 Facebook social networks with respect to network
properties including diameter, connected components, community structure, and
multiple node centrality measures. Experiments with our implementations of the
sparsification methods (using the open-source network analysis tool suite
NetworKit) show that many network properties can be preserved down to about
20\% of the original set of edges. Furthermore, the experimental results allow
us to differentiate the behavior of different methods and show which method is
suitable with respect to which property. Our Local Degree method is fast enough
for large-scale networks and performs well across a wider range of properties
than previously proposed methods.
