Graph filters that transform prior node values to posterior scores via edge propagation often support graph mining tasks affecting humans, such as recommendation and ranking. Thus, it is important to make them fair in terms of satisfying statistical parity constraints between groups of nodes (e.g., distribute score mass between genders proportionally to their representation). To achieve this while minimally perturbing the original posteriors, we introduce a filter-aware universal approximation framework for posterior objectives. This defines appropriate graph neural networks trained at runtime to be similar to filters but also locally optimize a large class of objectives, including fairness-aware ones. Experiments on a collection of 8 filters and 5 graphs show that our approach performs equally well or better than alternatives in meeting parity constraints while preserving the AUC of score-based community member recommendation and creating minimal utility loss in prior diffusion.

翻译：通过边缘传播将先前节点值转换为后端分数的图形过滤器往往支持影响人类的图表采矿任务,例如建议和排名。因此,在满足各节点组之间统计均等限制方面,必须做到公平(例如,按性别比例分配分数质量),要做到这一点,同时尽量减少对原始后端的干扰,我们为后端目标引入一个过滤器认知通用近似框架。这界定了在运行时经过培训的适当图形神经网络,类似于过滤器,但也优化了地方上的大量目标,包括公平觉悟目标。对8个过滤器和5个图表的收集进行的实验显示,在满足均等限制方面,我们的方法与替代方法一样好或更好,同时保留澳大利亚大学基于分数的社区成员建议,并在先前的传播方面造成最小的效用损失。</s>