  Our Microbiome Network Alignment Algorithm (MiNAA) aligns two microbial
networks using a combination of the GRAph ALigner (GRAAL) algorithm and the
Hungarian algorithm. Network alignment algorithms find pairs of nodes (one node
from the first network and the other node from the second network) that have
the highest similarity. Traditionally, similarity has been defined as
topological similarity such that the neighborhoods around the two nodes are
similar. Recent implementations of network alignment methods such as NETAL and
L-GRAAL also include measures of biological similarity, yet these methods are
restricted to one specific type of biological similarity (e.g. sequence
similarity in L-GRAAL). Our work extends existing network alignment
implementations by allowing any type of biological similarity to be input by
the user. This flexibility allows the user to choose whatever measure of
biological similarity is suitable for the study at hand. In addition, unlike
most existing network alignment methods that are tailored for protein or gene
interaction networks, our work is the first one suited for microbiome networks.
