Bipartite Graph Variational Auto-Encoder with Fair Latent Representation to Account for Sampling Bias in Ecological Networks

arXiv:2403.02011v1 Announce Type: cross
Abstract: 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 …

abstract arxiv auto bias challenges cs.lg cs.si embeddings encoder fair graph networks plants representation sampling stat.ml studying type