  Graphical models are widely used to study complex multivariate biological
systems. Network inference algorithms aim to reverse-engineer such models from
noisy experimental data. It is common to assess such algorithms using
techniques from classifier analysis. These metrics, based on ability to
correctly infer individual edges, possess a number of appealing features
including invariance to rank-preserving transformation. However, regulation in
biological systems occurs on multiple scales and existing metrics do not take
into account the correctness of higher-order network structure. In this paper
novel performance scores are presented that share the appealing properties of
existing scores, whilst capturing ability to uncover regulation on multiple
scales. Theoretical results confirm that performance of a network inference
algorithm depends crucially on the scale at which inferences are to be made; in
particular strong local performance does not guarantee accurate reconstruction
of higher-order topology. Applying these scores to a large corpus of data from
the DREAM5 challenge, we undertake a data-driven assessment of estimator
performance. We find that the ``wisdom of crowds'' network, that demonstrated
superior local performance in the DREAM5 challenge, is also among the best
performing methodologies for inference of regulation on multiple length scales.
MATLAB R2013b code "net_assess" is provided as Supplement.
