  Biological network alignment identifies topologically and functionally
conserved regions between networks of different species. It encompasses two
algorithmic steps: node cost function (NCF), which measures similarities
between nodes in different networks, and alignment strategy (AS), which uses
these similarities to rapidly identify high-scoring alignments. Different
methods use both different NCFs and different ASs. Thus, it is unclear whether
the superiority of a method comes from its NCF, its AS, or both. We already
showed on MI-GRAAL and IsoRankN that combining NCF of one method and AS of
another method can lead to a new superior method. Here, we evaluate MI-GRAAL
against newer GHOST to potentially further improve alignment quality. Also, we
approach several important questions that have not been asked systematically
thus far. First, we ask how much of the node similarity information in NCF
should come from sequence data compared to topology data. Existing methods
determine this more-less arbitrarily, which could affect the resulting
alignment(s). Second, when topology is used in NCF, we ask how large the size
of the neighborhoods of the compared nodes should be. Existing methods assume
that larger neighborhood sizes are better.
  We find that MI-GRAAL's NCF is superior to GHOST's NCF, while the performance
of the methods' ASs is data-dependent. Thus, the combination of MI-GRAAL's NCF
and GHOST's AS could be a new superior method for certain data. Also, which
amount of sequence information is used within NCF does not affect alignment
quality, while the inclusion of topological information is crucial. Finally,
larger neighborhood sizes are preferred, but often, it is the second largest
size that is superior, and using this size would decrease computational
complexity.
  Together, our results give several general recommendations for a fair
evaluation of network alignment methods.
