双图变分自编码器与公平潜在表示用于生态网络中采样偏差的处理
Bipartite Graph Variational Auto-Encoder with Fair Latent Representation to Account for Sampling Bias in Ecological Networks
AI总结：
本文提出了一种双图变分自编码器，通过引入HSIC来处理生态网络中的采样偏差，以获得公平的潜在表示。
摘要：
公民科学监测项目可以生成大量有价值的数据，但常常受到采样偏差的影响。我们专注于一个记录植物-传粉者相互作用的公民科学项目，目标是学习能够总结观察到的相互作用的嵌入表示，同时考虑这种偏差。在我们的方法中，植物和传粉者物种基于其相互作用的概率进行嵌入。这些嵌入是通过针对双图的变分图自编码器的适应性方法得出的。为了减轻采样偏差的影响，我们结合了希尔伯特-施瓦茨独立准则（HSIC），以确保与与采样过程相关的连续变量的独立性。这使我们能够将通常在社会科学中探讨的公平性视角纳入生态数据的分析中。我们通过模拟研究验证了我们的方法，复制了采样过程的关键方面，并通过Spipoll数据集展示了其应用和有效性。
英文摘要：
Citizen science monitoring programs can generate large amounts of valuable data, but are often affected by sampling bias. We focus on a citizen science initiative that records plant-pollinator interactions, with the goal of learning embeddings that summarize the observed interactions while accounting for such bias. In our approach, plant and pollinator species are embedded based on their probability of interaction. These embeddings are derived using an adaptation of variational graph autoencoders for bipartite graphs. To mitigate the influence of sampling bias, we incorporate the Hilbert-Schmidt Independence Criterion (HSIC) to ensure independence from continuous variables related to the sampling process. This allows us to integrate a fairness perspective, commonly explored in the social sciences, into the analysis of ecological data. We validate our method through a simulation study replicating key aspects of the sampling process and demonstrate its applicability and effectiveness using the Spipoll dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
