  Approximate Bayesian Computation (ABC) now serves as one of the major
strategies to perform model choice and parameter inference on models with
intractable likelihoods. An essential component of ABC involves comparing a
large amount of simulated data with the observed data through summary
statistics. To avoid the curse of dimensionality, summary statistic selection
is of prime importance, and becomes even more critical when applying ABC to
mechanistic network models. Indeed, while many summary statistics can be used
to encode network structures, their computational complexity can be highly
variable. For large networks, computation of summary statistics can quickly
create a bottleneck, making the use of ABC difficult. To reduce this
computational burden and make the analysis of mechanistic network models more
practical, we investigated two questions in a model choice framework. First, we
studied the utility of cost-based filter selection methods to account for
different summary costs during the selection process. Second, we performed
selection using networks generated with a smaller number of nodes to reduce the
time required for the selection step. Our findings show that computationally
inexpensive summary statistics can be efficiently selected with minimal impact
on classification accuracy. Furthermore, we found that networks with a smaller
number of nodes can only be employed to eliminate a moderate number of
summaries. While this latter finding is network specific, the former is general
and can be adapted to any ABC application.
