  Biological network alignment (NA) aims to identify similar regions between
molecular networks of different species. NA can be local or global. Just as the
recent trend in the NA field, we also focus on global NA, which can be pairwise
(PNA) and multiple (MNA). PNA produces aligned node pairs between two networks.
MNA produces aligned node clusters between more than two networks. Recently,
the focus has shifted from PNA to MNA, because MNA captures conserved regions
between more networks than PNA (and MNA is thus considered to be more
insightful), though at higher computational complexity. The issue is that, due
to the different outputs of PNA and MNA, a PNA method is only compared to other
PNA methods, and an MNA method is only compared to other MNA methods.
Comparison of PNA against MNA must be done to evaluate whether MNA's higher
complexity is justified by its higher accuracy. We introduce a framework that
allows for this. We evaluate eight prominent PNA and MNA methods, on synthetic
and real-world biological networks, using topological and functional alignment
quality measures. We compare PNA against MNA in both a pairwise (native to PNA)
and multiple (native to MNA) manner. PNA is expected to perform better under
the pairwise evaluation framework. Indeed this is what we find. MNA is expected
to perform better under the multiple evaluation framework. Shockingly, we find
this not to always hold; PNA is often better than MNA in this framework,
depending on the choice of evaluation test.
