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.

翻译：我们提出一种基于图嵌入的二部网络表征方法，旨在应对生态网络研究中的挑战——例如植物与传粉者相互作用的网络，其中需考虑众多协变量，尤其是要控制采样偏差。我们将变分图自编码器方法推广至二部图场景，从而在潜在空间中生成嵌入，使两类节点基于其连接概率定位。借鉴社会学中常见的公平性框架，我们将其转化应用于生态学中的采样偏差问题。通过将希尔伯特-施密特独立性准则作为额外惩罚项纳入优化损失函数，确保潜在空间结构与采样过程相关的连续变量相互独立。最后，我们展示了该方法应用于Spipoll数据集（一个多观测者参与的植物-传粉者相互作用公民科学监测项目，易受采样偏差影响）时，如何改变我们对生态网络的理解。