  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.
