Bipartite Graph Variational Auto-Encoder with Fair Latent Representation
  to Account for Sampling Bias in Ecological Networks
Emre Anakok Pierre Barbillon Colin Fontaine Elisa Thebault
We propose a method to represent bipartite networks using graph embeddings
tailored to tackle the challenges of studying ecological networks, such as the
ones linking plants and pollinators, where many covariates need to be accounted
for, in particular to control for sampling bias. We adapt the variational graph
auto-encoder approach to the bipartite case, which enables us to generate
embeddings in a latent space where the two sets of nodes are positioned based
on their probability of connection. We translate the fairness framework
commonly considered in sociology in order to address sampling bias in ecology.
By incorporating the Hilbert-Schmidt independence criterion (HSIC) as an
additional penalty term in the loss we optimize, we ensure that the structure
of the latent space is independent of continuous variables, which are related
to the sampling process. Finally, we show how our approach can change our
understanding of ecological networks when applied to the Spipoll data set, a
citizen science monitoring program of plant-pollinator interactions to which
many observers contribute, making it prone to sampling bias.