We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i.e., the tendency of linked nodes to have similar attributes. Such assortativity is often induced by homophily, the tendency for nodes of similar properties to connect. Homophily can be common in social networks where systemic factors have forced individuals into communities which share a sensitive attribute. Through synthetic graphs, we study the interplay between locally occurring homophily and fair predictions, finding that not all node neighborhoods are equal in this respect -- neighborhoods dominated by one category of a sensitive attribute often struggle to obtain fair treatment, especially in the case of diverging local class and sensitive attribute homophily. After determining that a relationship between local homophily and fairness exists, we investigate if the issue of unfairness can be associated to the design of the applied GNN model. We show that by adopting heterophilous GNN designs capable of handling disassortative group labels, group fairness in locally heterophilous neighborhoods can be improved by up to 25% over homophilous designs in real and synthetic datasets.
翻译:我们研究图表神经网络(GNNs)的节点分类任务,建立以统计均等和机会平等衡量的团体公平与地方的相容性之间的联系,即链接节点具有相似属性的倾向。例如,类似属性的交点往往由同质、类似属性的节点连接的倾向引起。在系统性因素迫使个人进入敏感属性的社群的社会网络中,同质可常见。我们通过合成图表,研究当地发生的同质和公平预测之间的相互作用,发现并非所有节点社区在这方面都是平等的 -- -- 由某类敏感属性主导的社区往往为获得公平待遇而挣扎,特别是在不同地方等级和敏感属性同质的情况下。在确定本地同质和公平之间的关系后,我们调查不公平问题是否与应用的GNN模式的设计有关。我们通过采用能够处理超度群体标签的超度性GNNN设计来改进当地超度的GNNs。在真实和合成区域中,25 % 的集团公平性合成区数据可以通过本地超度设计来改进。