  The abundance of intestinal flora is closely related to human diseases, but
diseases are not caused by a single gut microbe. Instead, they result from the
complex interplay of numerous microbial entities. This intricate and implicit
connection among gut microbes poses a significant challenge for disease
prediction using abundance information from OTU data. Recently, several methods
have shown potential in predicting corresponding diseases. However, these
methods fail to learn the inner association among gut microbes from different
hosts, leading to unsatisfactory performance. In this paper, we present a novel
architecture, Unsupervised Multi-graph Merge Adversarial Network (UMMAN). UMMAN
can obtain the embeddings of nodes in the Multi-Graph in an unsupervised
scenario, so that it helps learn the multiplex association. Our method is the
first to combine Graph Neural Network with the task of intestinal flora disease
prediction. We employ complex relation-types to construct the Original-Graph
and disrupt the relationships among nodes to generate corresponding
Shuffled-Graph. We introduce the Node Feature Global Integration (NFGI) module
to represent the global features of the graph. Furthermore, we design a joint
loss comprising adversarial loss and hybrid attention loss to ensure that the
real graph embedding aligns closely with the Original-Graph and diverges from
the Shuffled-Graph. Comprehensive experiments on five classical OTU gut
microbiome datasets demonstrate the effectiveness and stability of our method.
(We will release our code soon.)
