  We aim to learn the functional co-response group: a group of taxa whose
co-response effect (the representative characteristic of the group showing the
total topological abundance of taxa) co-responds (associates well
statistically) to a functional variable. Different from the state-of-the-art
method, we model the soil microbial community as an ecological co-occurrence
network with the taxa as nodes (weighted by their abundance) and their
relationships (a combination from both spatial and functional ecological
aspects) as edges (weighted by the strength of the relationships). Then, we
design a method called gFlora which notably uses graph convolution over this
co-occurrence network to get the co-response effect of the group, such that the
network topology is also considered in the discovery process. We evaluate
gFlora on two real-world soil microbiome datasets (bacteria and nematodes) and
compare it with the state-of-the-art method. gFlora outperforms this on all
evaluation metrics, and discovers new functional evidence for taxa which were
so far under-studied. We show that the graph convolution step is crucial to
taxa with relatively low abundance (thus removing the bias towards taxa with
higher abundance), and the discovered bacteria of different genera are
distributed in the co-occurrence network but still tightly connected among
themselves, demonstrating that topologically they fill different but
collaborative functional roles in the ecological community.
