FairWire: Fair Graph Generation
This work addresses fairness issues in graph generation and analysis, which is important for deploying unbiased algorithms in real-world decision systems, though it appears incremental by building on existing fairness and generative methods.
The paper tackled the problem of structural bias in both real and synthetic graphs, which can lead to unfair predictions in graph-based machine learning, and proposed FairWire, a framework that effectively mitigates this bias as validated by experiments on real-world networks.
Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structures in these algorithms has raised significant concerns for the deployment of them in real-world decision systems. In addition, while synthetic graph generation has become pivotal for privacy and scalability considerations, the impact of generative learning algorithms on the structural bias has not yet been investigated. Motivated by this, this work focuses on the analysis and mitigation of structural bias for both real and synthetic graphs. Specifically, we first theoretically analyze the sources of structural bias that result in disparity for the predictions of dyadic relations. To alleviate the identified bias factors, we design a novel fairness regularizer that offers a versatile use. Faced with the bias amplification in graph generation models that is brought to light in this work, we further propose a fair graph generation framework, FairWire, by leveraging our fair regularizer design in a generative model. Experimental results on real-world networks validate that the proposed tools herein deliver effective structural bias mitigation for both real and synthetic graphs.