CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs
This addresses fairness issues in graph-based AI for domains with unlabeled network data, but it is incremental as it builds on existing frameworks like GraphSAGE.
The paper tackled bias in unsupervised graph representation learning due to node centrality, proposing CAFIN to reduce performance disparity across nodes. It achieved reductions in performance disparity ranging from 18% to 80% on various datasets with minimal fairness cost.
Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this context, the need to consider fairness and bias constraints while generating the representations has been well-motivated and studied to some extent in prior works. One major limitation of most of the prior works in this setting is that they do not aim to address the bias generated due to connectivity patterns in the graphs, such as varied node centrality, which leads to a disproportionate performance across nodes. In our work, we aim to address this issue of mitigating bias due to inherent graph structure in an unsupervised setting. To this end, we propose CAFIN, a centrality-aware fairness-inducing framework that leverages the structural information of graphs to tune the representations generated by existing frameworks. We deploy it on GraphSAGE (a popular framework in this domain) and showcase its efficacy on two downstream tasks - Node Classification and Link Prediction. Empirically, CAFIN consistently reduces the performance disparity across popular datasets (varying from 18 to 80% reduction in performance disparity) from various domains while incurring only a minimal cost of fairness.