CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs

arXiv:2304.04391v3 Announce Type: replace
Abstract: 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 …

abstract arxiv bias constraints context cs.ai cs.cy cs.lg data fairness generated graphs network processing representation representation learning type unsupervised