FAIRNESS-AWARE SELECTIVE SAMPLING ON ATTRIBUTED GRAPHS

Selective sampling is an online learning framework where the learner tries to detect the data samples whose labels can boost the performance maximally, and only the labels of chosen data samples are queried. While the design of selective sampling algorithms is extensively studied for independent data samples, the area is rather under-explored in the context of graphs. Furthermore, the limited number of existing graph-based approaches do not take into consideration the nodal attributes that are available in attributed graphs. In this study, existing online learning and selective sampling algorithms are modified to be used with graphs that have nodal features. Additionally, the bias in the results of original algorithms is investigated, and a novel bias reduction strategy is proposed that can be embedded into the dimensionality reduction step without incurring a significant complexity cost. Experiments for node classification are carried out on real social networks to showcase the advantages of incorporating nodal features, as well as the fairness-enhancement framework.