  Networks can model real-world systems in a variety of domains. Network
alignment (NA) aims to find a node mapping that conserves similar regions
between compared networks. NA is applicable to many fields, including
computational biology, where NA can guide the transfer of biological knowledge
from well- to poorly-studied species across aligned network regions. Existing
NA methods can only align static networks. However, most complex real-world
systems evolve over time and should thus be modeled as dynamic networks. We
hypothesize that aligning dynamic network representations of evolving systems
will produce superior alignments compared to aligning the systems' static
network representations, as is currently done. For this purpose, we introduce
the first ever dynamic NA method, DynaMAGNA++. This proof-of-concept dynamic NA
method is an extension of a state-of-the-art static NA method, MAGNA++. Even
though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation
across the aligned networks, MAGNA++ conserves static edges and similarity
between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges
(events) and similarity between evolving node neighborhoods. For this purpose,
we introduce the first ever measure of dynamic edge conservation and rely on
our recent measure of dynamic node conservation. Importantly, the two dynamic
conservation measures can be optimized using any state-of-the-art NA method and
not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to
static NA, under fair comparison conditions, on synthetic and real-world
networks, in computational biology and social network domains. DynaMAGNA++ is
parallelized and it includes a user-friendly graphical interface.
