  The interactions among the constituent members of a microbial community play
a major role in determining the overall behavior of the community and the
abundance levels of its members. These interactions can be modeled using a
network whose nodes represent microbial taxa and edges represent pairwise
interactions. A microbial network is a weighted graph that is constructed from
a sample-taxa count matrix, and can be used to model co-occurrences and/or
interactions of the constituent members of a microbial community. The nodes in
this graph represent microbial taxa and the edges represent pairwise
associations amongst these taxa. A microbial network is typically constructed
from a sample-taxa count matrix that is obtained by sequencing multiple
biological samples and identifying taxa counts. From large-scale microbiome
studies, it is evident that microbial community compositions and interactions
are impacted by environmental and/or host factors. Thus, it is not unreasonable
to expect that a sample-taxa matrix generated as part of a large study
involving multiple environmental or clinical parameters can be associated with
more than one microbial network. However, to our knowledge, microbial network
inference methods proposed thus far assume that the sample-taxa matrix is
associated with a single network.
